Well log correlation and propagation system

ABSTRACT

A system can include a processor; memory operatively coupled to the processor; and processor-executable instructions stored in the memory to instruct the system to: receive a marker on a well log for a well in a geographic region; and iteratively propagate the marker automatically to a plurality of well logs for other wells in the geographic region.

RELATED APPLICATIONS

This application claims priority to and the benefit of a US ProvisionalApplication having Ser. No. 62/660,651, filed 20 Apr. 2018, which isincorporated by reference herein.

BACKGROUND

Interpretation is a process that involves analysis of data to identifyand locate various subsurface structures (e.g., horizons, faults,geobodies, etc.) in a geologic environment. Various types of structures(e.g., stratigraphic formations) may be indicative of hydrocarbon trapsor flow channels, as may be associated with one or more reservoirs(e.g., fluid reservoirs). In the field of resource extraction,enhancements to interpretation can allow for construction of a moreaccurate model of a subsurface region, which, in turn, may improvecharacterization of the subsurface region for purposes of resourceextraction. Characterization of one or more subsurface regions in ageologic environment can guide, for example, performance of one or moreoperations (e.g., field operations, etc.).

SUMMARY

A system can include a processor; memory operatively coupled to theprocessor; and processor-executable instructions stored in the memory toinstruct the system to: receive a marker on a well log for a well in ageographic region; and iteratively propagate the marker automatically toa plurality of well logs for other wells in the geographic region. Amethod can include receiving a marker on a well log for a well in ageographic region; and iteratively propagating the marker automaticallyto a plurality of well logs for other wells in the geographic region.One or more computer-readable storage media can includecomputer-executable instructions executable to instruct a computingsystem to: receive a marker on a well log for a well in a geographicregion; and iteratively propagate the marker automatically to aplurality of well logs for other wells in the geographic region. Variousother apparatuses, systems, methods, etc., are also disclosed.

This summary is provided to introduce a selection of concepts that arefurther described below in the detailed description. This summary is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in limiting the scope ofthe claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the described implementations can be morereadily understood by reference to the following description taken inconjunction with the accompanying drawings.

FIG. 1 illustrates an example system that includes various componentsfor modeling a geologic environment and various equipment associatedwith the geologic environment;

FIG. 2 illustrates an example of an interpretation method;

FIG. 3 illustrates examples of equipment including examples of downholetools and examples of bores;

FIG. 4 illustrates examples of equipment including examples of downholetools;

FIG. 5 illustrates examples of well logs along with a process ofpredicting one or more picks on a series of context logs from a singlelog that has associated user provided interpretation information (e.g.,a user interpreted log);

FIG. 6 illustrates an example of a depth search range with respect to amarker on a well log;

FIG. 7 illustrates an example of propagation via a fast dynamic timewarping (FastDTW) approach;

FIG. 8 illustrates an example of a map that includes various welllocations and a location of a selected well;

FIG. 9 illustrates the map of FIG. 8 where an example of a spanning treethat includes various well locations;

FIG. 10 illustrates an example of propagation with respect to a portionof a tree;

FIG. 11 illustrates an example of adjusting a search range;

FIG. 12 illustrates an example of a method that includes calculatingconfidence based at least in part on a value of a FastDTW approach;

FIG. 13 illustrates examples of logs as organized randomly and by aspanning tree;

FIG. 14 illustrates an example of a graphical user interface (GUI) thatincludes connected wells with confidence information;

FIG. 15 illustrates an example of a graphical user interface (GUI) thatincludes connected wells with confidence information;

FIG. 16 illustrates an example of a graphical user interface (GUI) thatincludes a panel with logs for various wells;

FIG. 17 illustrates an example of a graphical user interface (GUI) thatincludes a background map and well related information rendered thereon;

FIG. 18 illustrates an example of a graphical user interface (GUI) thatincludes various examples of logs for various wells where one of thewells is a selected well;

FIG. 19 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells of FIG. 18 and a graphical controlassociated with one of the wells;

FIG. 20 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells of FIG. 18 and a graphical control forpropagating information of the selected well to one or more of the otherwells;

FIG. 21 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells of FIG. 18 where graphics indicate howinformation is propagated from the selected well to the other wells;

FIG. 22 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells and information of FIG. 21 and confidenceinformation;

FIG. 23 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells and information of FIG. 22 and a graphicalcontrol for rendering information about tops of one of the wells;

FIG. 24 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells and information of FIG. 22 and a graphicalcontrol for rendering uncertainty information about tops of one of thewells;

FIG. 25 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells of FIG. 24 and a graphical control thatselects a portion of a log of one of the wells;

FIG. 26 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells of FIG. 25 and a graphical control thatselects another portion of the log of the one of the wells;

FIG. 27 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells of FIG. 26 and a graphical control forrendering information about a modified top of the one of the wells;

FIG. 28 illustrates an example of a graphical user interface (GUI) thatincludes various examples of logs for various wells of FIG. 27 and agraphical control for editing one or more connections between wells;

FIG. 29 illustrates an example of a graphical user interface (GUI) thatincludes various examples of logs for various wells of FIG. 28 and thegraphical control for deleting one or more connections between wells;

FIG. 30 illustrates an example of a graphical user interface (GUI) thatincludes various examples of logs for various wells of FIG. 29 where oneor more connections are deleted between wells;

FIG. 31 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells of FIG. 26 and a graphical control for aflattening operation;

FIG. 32 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells of FIG. 31 as adjusted per the flatteningoperation;

FIG. 33 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells of FIG. 32 and a graphical control for arestoration of a depth view operation;

FIG. 34 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells of FIG. 33 as restored per the restorationof the depth view operation;

FIG. 35 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells of FIG. 26 and a graphical control foruncertainty information;

FIG. 36 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells of FIG. 35 and rendered uncertaintyinformation;

FIG. 37 illustrates an example of a graphical user interface (GUI) thatincludes the logs and wells of FIG. 36 and a graphical control forvalidating tops in the rendered view;

FIG. 38 illustrates an example of a graphical user interface (GUI) thatincludes examples of logs and wells in a log panel that correspond tologs and wells in a strip panel that includes an adjustable window;

FIG. 39 illustrates an example of a graphical user interface (GUI) thatincludes examples of logs and wells in the log panel of FIG. 38 thatcorrespond to logs and wells in the strip panel of FIG. 38 and agraphical control for adjusting the adjustable window;

FIG. 40 illustrates an example of a graphical user interface (GUI) thatincludes examples of logs and wells in a log panel that correspond tologs and wells in the strip panel of FIG. 38 as selected via adjustmentof the adjustable window;

FIG. 41 illustrates an example of a graphical user interface (GUI) thatincludes examples of logs and wells and a graphical control foraccessing details of a selected well;

FIG. 42 illustrates an example of a method; and

FIG. 43 illustrates example components of a system and a networkedsystem.

DETAILED DESCRIPTION

This description is not to be taken in a limiting sense, but rather ismade merely for the purpose of describing the general principles of theimplementations. The scope of the described implementations should beascertained with reference to the issued claims.

FIG. 1 shows an example of a system 100 that includes various managementcomponents 110 to manage various aspects of a geologic environment 150(e.g., an environment that includes a sedimentary basin, a reservoir151, one or more faults 153-1, one or more geobodies 153-2, etc.). Forexample, the management components 110 may allow for direct or indirectmanagement of sensing, drilling, injecting, extracting, etc., withrespect to the geologic environment 150. In turn, further informationabout the geologic environment 150 may become available as feedback 160(e.g., optionally as input to one or more of the management components110).

In the example of FIG. 1 , the management components 110 include aseismic data component 112, an additional information component 114(e.g., well/logging data), a processing component 116, a simulationcomponent 120, an attribute component 130, an analysis/visualizationcomponent 142 and a workflow component 144. In operation, seismic dataand other information provided per the components 112 and 114 may beinput to the simulation component 120.

In an example embodiment, the simulation component 120 may rely onentities 122. Entities 122 may include earth entities or geologicalobjects such as wells, surfaces, bodies, reservoirs, etc. In the system100, the entities 122 can include virtual representations of actualphysical entities that are reconstructed for purposes of simulation. Theentities 122 may include entities based on data acquired via sensing,observation, etc. (e.g., the seismic data 112 and other information114). An entity may be characterized by one or more properties (e.g., ageometrical pillar grid entity of an earth model may be characterized bya porosity property). Such properties may represent one or moremeasurements (e.g., acquired data), calculations, etc.

In an example embodiment, the simulation component 120 may operate inconjunction with a software framework such as an object-based framework.In such a framework, entities may include entities based on pre-definedclasses to facilitate modeling and simulation. An example of anobject-based framework is the MICROSOFT .NET framework (Redmond,Washington), which provides a set of extensible object classes. In the.NET framework, an object class encapsulates a module of reusable codeand associated data structures. Object classes can be used toinstantiate object instances for use in by a program, script, etc. Forexample, borehole classes may define objects for representing boreholesbased on well data.

In the example of FIG. 1 , the simulation component 120 may processinformation to conform to one or more attributes specified by theattribute component 130, which may include a library of attributes. Suchprocessing may occur prior to input to the simulation component 120(e.g., consider the processing component 116). As an example, thesimulation component 120 may perform operations on input informationbased on one or more attributes specified by the attribute component130. In an example embodiment, the simulation component 120 mayconstruct one or more models of the geologic environment 150, which maybe relied on to simulate behavior of the geologic environment 150 (e.g.,responsive to one or more acts, whether natural or artificial). In theexample of FIG. 1 , the analysis/visualization component 142 may allowfor interaction with a model or model-based results (e.g., simulationresults, etc.). As an example, output from the simulation component 120may be input to one or more other workflows, as indicated by a workflowcomponent 144.

As an example, the simulation component 120 may include one or morefeatures of a simulator such as the ECLIPSE reservoir simulator(Schlumberger Limited, Houston Texas), the INTERSECT reservoir simulator(Schlumberger Limited, Houston Texas), etc. As an example, a simulationcomponent, a simulator, etc. may include features to implement one ormore meshless techniques (e.g., to solve one or more equations, etc.).As an example, a reservoir or reservoirs may be simulated with respectto one or more enhanced recovery techniques (e.g., consider a thermalprocess such as steam-assisted gravity drainage (SAGD), etc.).

In an example embodiment, the management components 110 may includefeatures of a framework such as the PETREL seismic to simulationsoftware framework (Schlumberger Limited, Houston, Texas). The PETRELframework provides components that allow for optimization of explorationand development operations. The PETREL framework includes seismic tosimulation software components that can output information for use inincreasing reservoir performance, for example, by improving asset teamproductivity. Through use of such a framework, various professionals(e.g., geophysicists, geologists, and reservoir engineers) can developcollaborative workflows and integrate operations to streamlineprocesses. Such a framework may be considered an application and may beconsidered a data-driven application (e.g., where data is input forpurposes of modeling, simulating, etc.).

In an example embodiment, various aspects of the management components110 may include add-ons or plug-ins that operate according tospecifications of a framework environment. For example, a frameworkenvironment marketed as the OCEAN framework environment (SchlumbergerLimited, Houston, Texas) allows for integration of add-ons (or plug-ins)into a PETREL framework workflow. In an example embodiment, variouscomponents may be implemented as add-ons (or plug-ins) that conform toand operate according to specifications of a framework environment(e.g., according to application programming interface (API)specifications, etc.).

FIG. 1 also shows an example of a framework 170 that includes a modelsimulation layer 180 along with a framework services layer 190, aframework core layer 195 and a modules layer 175. The framework 170 mayinclude the OCEAN framework where the model simulation layer 180 is thePETREL model-centric software package that hosts OCEAN frameworkapplications. In an example embodiment, the PETREL software may beconsidered a data-driven application. The PETREL software can include aframework for model building and visualization.

As an example, seismic data may be processed using a framework such asthe OMEGA framework (Schlumberger Limited, Houston, TX). The OMEGAframework provides features that can be implemented for processing ofseismic data, for example, through prestack seismic interpretation andseismic inversion. A framework may be scalable such that it enablesprocessing and imaging on a single workstation, on a massive computecluster, etc. As an example, one or more techniques, technologies, etc.described herein may optionally be implemented in conjunction with aframework such as, for example, the OMEGA framework.

A framework for processing data may include features for 2D line and 3Dseismic surveys. Modules for processing seismic data may includefeatures for prestack seismic interpretation (PSI), optionally pluggableinto a framework such as the OCEAN framework. A workflow may bespecified to include processing via one or more frameworks, plug-ins,add-ons, etc. A workflow may include quantitative interpretation, whichmay include performing pre- and poststack seismic data conditioning,inversion (e.g., seismic to properties and properties to syntheticseismic), wedge modeling for thin-bed analysis, amplitude versus offset(AVO) and amplitude versus angle (AVA) analysis, reconnaissance, etc. Asan example, a workflow may aim to output rock properties based at leastin part on processing of seismic data. As an example, various types ofdata may be processed to provide one or more models (e.g., earthmodels). For example, consider processing of one or more of seismicdata, well data, electromagnetic and magnetic telluric data, reservoirdata, etc.

As an example, a framework may include features for implementing one ormore mesh generation techniques. For example, a framework may include aninput component for receipt of information from interpretation ofseismic data, one or more attributes based at least in part on seismicdata, log data, image data, etc. Such a framework may include a meshgeneration component that processes input information, optionally inconjunction with other information, to generate a mesh.

In the example of FIG. 1 , the model simulation layer 180 may providedomain objects 182, act as a data source 184, provide for rendering 186and provide for various user interfaces 188. Rendering 186 may provide agraphical environment in which applications can display their data whilethe user interfaces 188 may provide a common look and feel forapplication user interface components.

As an example, the domain objects 182 can include entity objects,property objects and optionally other objects. Entity objects may beused to geometrically represent wells, surfaces, bodies, reservoirs,etc., while property objects may be used to provide property values aswell as data versions and display parameters. For example, an entityobject may represent a well where a property object provides loginformation as well as version information and display information(e.g., to display the well as part of a model).

In the example of FIG. 1 , data may be stored in one or more datasources (or data stores, generally physical data storage devices), whichmay be at the same or different physical sites and accessible via one ormore networks. The model simulation layer 180 may be configured to modelprojects. As such, a particular project may be stored where storedproject information may include inputs, models, results and cases. Thus,upon completion of a modeling session, a user may store a project. At alater time, the project can be accessed and restored using the modelsimulation layer 180, which can recreate instances of the relevantdomain objects.

In the example of FIG. 1 , the geologic environment 150 may includelayers (e.g., stratification) that include a reservoir 151 and one ormore other features such as the fault 153-1, the geobody 153-2, etc. Asan example, the geologic environment 150 may be outfitted with a varietyof sensors, detectors, actuators, etc. For example, equipment 152 mayinclude communication circuitry to receive and to transmit informationwith respect to one or more networks 155. Such information may includeinformation associated with downhole equipment 154, which may beequipment to acquire information, to assist with resource recovery, etc.Other equipment 156 may be located remote from a well site and includesensing, detecting, emitting or other circuitry. Such equipment mayinclude storage and communication circuitry to store and to communicatedata, instructions, etc. As an example, one or more satellites may beprovided for purposes of communications, data acquisition, etc. Forexample, FIG. 1 shows a satellite in communication with the network 155that may be configured for communications, noting that the satellite mayadditionally or alternatively include circuitry for imagery (e.g.,spatial, spectral, temporal, radiometric, etc.).

FIG. 1 also shows the geologic environment 150 as optionally includingequipment 157 and 158 associated with a well that includes asubstantially horizontal portion that may intersect with one or morefractures 159. For example, consider a well in a shale formation thatmay include natural fractures, artificial fractures (e.g., hydraulicfractures) or a combination of natural and artificial fractures. As anexample, a well may be drilled for a reservoir that is laterallyextensive. In such an example, lateral variations in properties,stresses, etc. may exist where an assessment of such variations mayassist with planning, operations, etc. to develop a laterally extensivereservoir (e.g., via fracturing, injecting, extracting, etc.). As anexample, the equipment 157 and/or 158 may include components, a system,systems, etc. for fracturing, seismic sensing, analysis of seismic data,assessment of one or more fractures, etc.

As mentioned, the system 100 may be used to perform one or moreworkflows. A workflow may be a process that includes a number ofworksteps. A workstep may operate on data, for example, to create newdata, to update existing data, etc. As an example, a may operate on oneor more inputs and create one or more results, for example, based on oneor more algorithms. As an example, a system may include a workfloweditor for creation, editing, executing, etc. of a workflow. In such anexample, the workflow editor may provide for selection of one or morepre-defined worksteps, one or more customized worksteps, etc. As anexample, a workflow may be a workflow implementable in the PETRELsoftware, for example, that operates on seismic data, seismicattribute(s), etc. As an example, a workflow may be a processimplementable in the OCEAN framework. As an example, a workflow mayinclude one or more worksteps that access a module such as a plug-in(e.g., external executable code, etc.).

As an example, a framework may be implemented within or in a manneroperatively coupled to the DELFI cognitive exploration and production(E&P) environment (Schlumberger Limited, Houston, Texas), which is asecure, cognitive, cloud-based collaborative environment that integratesdata and workflows with digital technologies, such as artificialintelligence and machine learning. As an example, such an environmentcan provide for operations that involve one or more computationalframeworks. For example, various types of computational frameworks maybe utilized within an environment such as a drilling plan framework, aseismic-to-simulation framework (e.g., PETREL framework, SchlumbergerLimited, Houston, Texas), a measurements framework (e.g., TECHLOGframework, Schlumberger Limited, Houston, Texas), a mechanical earthmodeling (MEM) framework (PETROMOD framework, Schlumberger Limited,Houston, Texas), an exploration risk, resource, and value assessmentframework (e.g., GEOX, Schlumberger Limited, Houston, Texas), areservoir simulation framework (INTERSECT, Schlumberger Limited,Houston, Texas), a surface facilities framework (e.g., PIPESIM,Schlumberger Limited, Houston, Texas). As an example, one or moremethods may be implemented at least in part via a framework (e.g., acomputational framework) and/or an environment (e.g., a computationalenvironment).

As an example, the system 100 may be implemented for performing one ormore workflows associated with sequence stratigraphy. For example,basin-filling sedimentary deposits can be organized as sequences and canbe interpreted in a depositional framework of eustasy, sedimentation andsubsidence through time in to correlate strata and predict thestratigraphy of relatively unknown areas. Sequences may tend to showcyclicity of changes in relative sea level and widespreadunconformities, processes of sedimentation and sources of sediments,climate and tectonic activity over time. Sequence stratigraphic analysescan promote a more thorough understanding of an evolution of a basinand, for example, allow for interpretations of potential source rocksand reservoir rocks in frontier areas (e.g., having seismic data butlittle well data) and in more mature hydrocarbon provinces. Predictionof reservoir continuity can be of facilitated via sequence stratigraphy,particularly in mature hydrocarbon provinces.

A framework (e.g., TECHLOG, PETREL, etc.) can provide for performing oneor more types of well correlation workflows, which can includeconnection of points from well to well, for example, where data indicatethat the points (e.g., locations) are likely to have been deposited at acommon chronostratigraphic time and/or possess similar and/or relatedcharacteristics. A framework can include well correlation features thatcan display logs, core images, seismic data, grid data, and completionsand simulation results, which may be played through time. As an example,such a framework may be utilized to geosteer horizontal and highlydeviated wells with one or more logging while drilling (LWD) tools,optionally in real time. As an example, deviated wells may be displayedoverlain on seismic or 3D grid properties.

As an example, the PETREL framework can allow for cross sections thatcan be interactively created and shared across one or more projects and,for example, be directly edited in a 2D or map window (e.g., via one ormore graphical user interfaces). As an example, a GUI tool can providefor picking features (e.g., generating a marker that is a point ofinterest at a certain depth on a log), estimating logs by trained neuralnetworks, and performing interactive log conditioning that canfacilitate robust stratigraphic interpretation. Computational resourcescan allow the PETREL framework to handle advanced visualization, forexample, for thousands of wells simultaneously.

Various features of the PETREL framework provide for interpretation ofdiscrete properties interactively; automatically (or manually) pickingand editing well tops on a cross section and visualization of effectsdirectly in 3D and vice versa; editing existing logs and/or generatingnew ones from a number of curves by using a well log calculator, logeditor, or interactive log conditioning toolbar; generating ghost curvesfor multiple logs simultaneously, for example, with stretch and squeezetools and automatic drop of markers; displaying logs, core images, pointdata, image interpretations (e.g., rose diagrams and tadpoles) from theSchlumberger FMI fullbore formation microimager, FMI-HD high-definitionformation microimager, QUANTA GEO photorealistic reservoir geologyservice, checkshots, and synthetic seismograms; interpreting raster logswith high resolution; creating backdrop seismic data, generic surfaces,3D grid geometry, 3D grid properties with optional transparency,completions, and simulation results with an associated dynamic timeplayer; visualizing and interpreting on deviated wells in a crosssection; and geosteering horizontal and/or highly deviated wells,optionally in real time, for example, with multiple measurements andborehole images being rendered to one or more displays (e.g., via one ormore GUIs).

As an example, information acquired by a tool (e.g., a borehole tool, adownhole tool, etc.) may be analyzed using a framework such as theTECHLOG framework. The TECHLOG framework includes: core system features;geology features; geomechanics features; geophysics features;petrophysics features; production features; reservoir engineeringfeatures; and shale features.

Data-based interpretation may aim to identify and/or classify one ormore subsurface boundaries based at least in part on one or moreparameters, which can include one or more dip parameters (e.g., angle ormagnitude, azimuth, etc.). As an example, various types of features(e.g., sedimentary bedding, faults and fractures, cuestas, igneous dikesand sills, metamorphic foliation, etc.) may be described at least inpart by angle, at least in part by azimuth, etc.

As to seismology, sensor data concerning P-waves and/or S-waves may beutilized to characterize a subsurface region. A P-wave is an elasticbody wave or sound wave in which particles oscillate in the directionthe wave propagates. An S-wave is an elastic body wave in whichparticles oscillate perpendicular to the direction in which the wavepropagates. As an example, P-waves incident on an interface at otherthan normal incidence can produce reflected and transmitted S-waves, inthat case known as converted waves.

As an example, a portion or portions of a formation or formations of abasin or basins may exhibit anisotropy. As examples of parameters thatcan characterize anisotropy of media (e.g., seismic anisotropy, etc.),consider the Thomsen parameters ε, δ and γ. The Thomsen parameter δ cancharacterize anisotropy of a near-vertical P-wave; as to the Thomsenparameter ε, it can characterize P-wave anisotropy; and, as to theThomsen parameter γ, it can characterize S-wave anisotropy.

As an example, a method can include inverting, for example, from data toa model, which may be a structural model of a geologic region (e.g., aportion of a basin, etc.). An inversion process can be performedutilizing various types of information, particularly acquired data fromone or more sensors where the inversion process aims to generate a modelthat exhibits at least some consistency with the information. Aninversion process can include solving an inverse problem, which may beformulated via various equations and solved using computationalresources (e.g., one or more processors, memory, etc.). In seismology,surface seismic data, vertical seismic profiles and well log data can beused to perform inversion, the result of which can be a model of Earthlayers and their thickness, density and P- and S-wave velocities.Inversion can benefit from known information and/or behaviors. As todata quality, inversion can benefit from a high signal-to-noise ratioand a large bandwidth.

A well log can be a record (e.g., a recording) of well log signalsand/or signal-based output. A well log can be a record of results ofelectronic measurements of physical quantities acquired in a continuumfashion (e.g., time series and/or depth series), which may be at one ormore different well depths.

As an example, an inversion technique may be applied to generate a modelthat may include one or more parameters such as one or more of theThomsen parameters. For example, one or more types of data may bereceived and used in solving an inverse problem that outputs a model(e.g., a reflectivity model, an impedance model, a fluid flow model,etc.). As an example, an inversion process may be a joint inversionwhere, for example, various types of data may be utilized to generate amodel.

As an example, seismic data may be processed in a technique called“depth imaging” to form an image (e.g., a depth image) of reflectionamplitudes in a depth domain for a particular target structure (e.g., ageologic subsurface region of interest).

As an example, seismic data may be processed to obtain an elastic modelpertaining to elastic properties of a geologic subsurface region. Forexample, consider elastic properties such as density, compressional (P)impedance, compression velocity (v_(p))-to-shear velocity (v_(s)) ratio,anisotropy, etc. As an example, an elastic model can provide variousinsights as to a surveyed region's lithology, reservoir quality, fluids,etc.

As an example, a computational framework can include features thatfacilitate interpretation of subsurface well logs by automaticallyand/or semi-automatically correlating points from one log to another.For example, given a log that has been interpreted by a human (e.g., inthe sense of having identified different points of interest in the log),a framework may automatically interpret a set of neighboring logs.

As mentioned, well log information may be processed using one or moreframeworks (e.g., consider the PETREL framework, the TECHLOG framework,etc.). As mentioned, the PETREL framework may automatically (ormanually) provide for picking and editing well tops on a cross section.Such an approach may be more robust for relatively flat formations whencompared to formations that can vary (e.g., across a basin, etc.). Forexample, a framework suited for relatively flat formations may generateresults that are uncertain for formations that include one or moreportions that are not relatively flat. Lateral geologic variations canconfound propagation of points (e.g., picked markers) from one log toanother log. As an example, a marker can be generated via picking suchthat a marker is a picked marker. For example, an interpretation processcan include rendering a log to a display and picking a feature of thelog where picking generates a marker that is a point of interest at acertain depth on the log (e.g., or a range of depths on the log, etc.).

As an example, a computational framework can provide for output ofuncertainty metrics associated with log correlation and/or logpropagation (e.g., well to well) where such a framework may enhancerobustness for formations that can include one or more types of lateralgeologic variations. As an example, such a computational framework caninclude a propagation mechanism that implements one or more minimumspanning trees (e.g., or optionally another graph-based propagation ortraversal structure), which may be initiated by a single seed ormultiple seeds. As an example, a computational framework may propagatein a neighbor to neighbor approach and/or in a more dispersed approach(e.g., with or without proximity constraints, etc.). As an example, in amultiple seed approach, two propagation mechanisms may “meet” where aboundary may be defined, which may be, for example, a boundary betweengeologically different regions (e.g., as to depth, lithology, etc.).

As an example, a seed can be a seed in depth (e.g., measured depth,total depth, etc., depending on circumstances, geometry, etc.). As anexample, a single log of a single well may be a source of one or moreseeds that can propagate in a manner that correlates the single log toone or more logs of one or more other wells. As an example, a method caninclude log correlation and log propagation where results thereof canfacilitate sequence stratigraphy and/or one or more other processes.

As an example, a computational framework can include features that,given a set of well logs in a field, minimize the number of picks(interpretations) a human (or humans) make manually. Such an approachmay reduce the number of picks, at least in part by iteratively andautomatically correlating one or more human picked logs to at least aportion of the other logs.

As an example, a computational framework may aim to reduce the number ofhuman picks by a factor of 10 or more (e.g., where a human performs lessthan approximately 10 percent of the work). As an example, such acomputational framework may automatically identify (e.g., highlight,etc.) one or more logs that can benefit from human intervention (e.g.,quality control), which may accelerate a correlation and propagationprocess. As an example, such a computational framework may automaticallyorders logs for improved visualization. As an example, such acomputational framework may assist one or more humans by automaticallyperforming tedious correlations and pointing the one or more humans to“interesting” areas (e.g., consider faults, pinch outs, etc.).

As an example, a fault can be a break or planar surface in brittle rockacross which there is observable displacement. For example, in aformation that has been faulted, various layers can be displaced. Insuch an example, where one portion is shifted upward on one side of thefault, a well log from a well in that portion may include picked markers(e.g., points) that are at a different depth from the surface thancorresponding markers (e.g., points) of a well log from a well that isto the other side of the fault. Such well logs may present particularissues as to correlation and/or propagation.

As to a pinch out (e.g., or pinch-out), it can be a type ofstratigraphic trap. For example, consider termination by thinning ortapering out (“pinching out”) of a reservoir against a nonporous sealingrock that creates a favorable geometry to trap hydrocarbons,particularly if the adjacent sealing rock is a source rock such as ashale. In such an example, as a trap may exist for collection ofhydrocarbons, a pinch out can be of particular interest. As an example,a pinch out can be a reduction in bed thickness resulting from onlappingstratigraphic sequences.

As an example, a method can include selecting a set of logs to correlate(e.g., a field or basin that includes hundreds of wells or thousands ofwells or more) where an computational algorithm can be applied to afraction of wells (e.g., a relatively small fraction such as 20 percentor less); defining picks or markers on a chosen input log (e.g., at adepth of 1502 m on a log for well ABC a marker XYZ may be picked/setmanually by a human); computing a minimum spanning tree (MST) on a graphdefined by locations of the well logs; propagating the input log markerto neighboring markers in the graph using a FastDTW (e.g., an approachto dynamic time warping (DTW)) that can take into account geologicalstructure trends; once the markers have been found in the neighbors,propagating to the neighbor's neighbors (as available); iterating untila desired portion of the graph has been processed and/or reached (e.g.,consider a breadth first search); after an input pick has beenpropagated to a desired portion of the graph, calculating confidencescore on the wells of the desired portion of the graph (e.g., asincluded in the MST); pointing a human (or humans) to pick/assess thosewith lowest confidence (e.g., ask to accept or revise predicted pick);repeating with one or more new picks such that for each well log therecan be two predictions; picking the predictions of each well log withthe highest confidence; and iterating until one or more criteria aresatisfied (e.g., error, number of iterations, confidence, etc.). As toDTW, it can be utilized for measuring similarity between two temporalsequences. As to the FastDTW approach, it can be utilized in a mannerthat demands lesser computational resources or that may generate resultsin lesser time than a basic DTW approach.

FIG. 2 shows an example of a method 210 that includes a reception block214 for receiving a selection well logs of a plurality of wells (e.g.,wellbores), a reception block 218 for receiving defined picks on aninput well log (e.g., one of the selected well logs), a computationblock 222 for computing a minimum spanning tree for at least a portionof the well logs, a propagation block 226 for propagating the input toothers in the tree, an output block 230 for outputting confidence andpredictions and for subsequent iterations optionally comparingconfidences per a comparison block 232, a decision block 234 fordeciding whether the confidences are acceptable (e.g., or that one ormore criteria are satisfied), a termination block 238 for terminatingthe method 210 and outputting one or more results thereof (e.g., forinput to one or more workflows, etc.) upon a “yes” decision at thedecision block 234, a notification block 242 for issuing a notificationor notifications as to one or more low confidence well logs (e.g., as tocorrelation of a pick or picks) upon a “no” decision at the decisionblock 234, and a reception block 246 for receiving at least oneadditional defined pick (e.g., a prior defined pick that was notutilized, a newly defined pick, etc.), which may be utilized in aniteration loop until the decision block 234 decides that confidence(e.g., or one or more other criteria) have been satisfied.

The method 210 is shown in FIG. 2 in association with variouscomputer-readable media (CRM) blocks 215, 219, 223, 227, 231, 235, 243and 247. Such blocks generally include instructions suitable forexecution by one or more processors (or cores) to instruct a computingdevice or system to perform one or more actions. While various blocksare shown, a single medium may be configured with instructions to allowfor, at least in part, performance of various actions of the method 210.As an example, a CRM block can be a computer-readable storage mediumthat is non-transitory, not a carrier wave and not a signal. As anexample, such blocks can include instructions that can be stored inmemory and can be executable by one or more of processors.

FIGS. 3 and 4 illustrate various examples of equipment that may beutilized in one or more workflows that include, at least in part,acquiring well log data. As an example, a method may be implementedduring use of such equipment, using data acquired by such equipment,etc.

FIG. 3 shows an example of a wellsite system 300 (e.g., at a wellsitethat may be onshore or offshore). As shown, the wellsite system 300 caninclude a mud tank 301 for holding mud and other material (e.g., wheremud can be a drilling fluid), a suction line 303 that serves as an inletto a mud pump 304 for pumping mud from the mud tank 301 such that mudflows to a vibrating hose 306, a drawworks 307 for winching drill lineor drill lines 312, a standpipe 308 that receives mud from the vibratinghose 306, a kelly hose 309 that receives mud from the standpipe 308, agooseneck or goosenecks 310, a traveling block 311, a crown block 313for carrying the traveling block 311 via the drill line or drill lines312, a derrick 314, a kelly 318 or a top drive 340, a kelly drivebushing 319, a rotary table 320, a drill floor 321, a bell nipple 322,one or more blowout preventors (BOPs) 323, a drillstring 325, a drillbit 326, a casing head 327 and a flow pipe 328 that carries mud andother material to, for example, the mud tank 301.

In the example system of FIG. 3 , a borehole 332 is formed in subsurfaceformations 330 by rotary drilling; noting that various exampleembodiments may also use directional drilling.

As shown in the example of FIG. 3 , the drillstring 325 is suspendedwithin the borehole 332 and has a drillstring assembly 350 that includesthe drill bit 326 at its lower end. As an example, the drillstringassembly 350 may be a bottom hole assembly (BHA).

The wellsite system 300 can provide for operation of the drillstring 325and other operations. As shown, the wellsite system 300 includes theplatform 311 and the derrick 314 positioned over the borehole 332. Asmentioned, the wellsite system 300 can include the rotary table 320where the drillstring 325 pass through an opening in the rotary table320.

As shown in the example of FIG. 3 , the wellsite system 300 can includethe kelly 318 and associated components, etc., or a top drive 340 andassociated components. As to a kelly example, the kelly 318 may be asquare or hexagonal metal/alloy bar with a hole drilled therein thatserves as a mud flow path. The kelly 318 can be used to transmit rotarymotion from the rotary table 320 via the kelly drive bushing 319 to thedrillstring 325, while allowing the drillstring 325 to be lowered orraised during rotation. The kelly 318 can pass through the kelly drivebushing 319, which can be driven by the rotary table 320. As an example,the rotary table 320 can include a master bushing that operativelycouples to the kelly drive bushing 319 such that rotation of the rotarytable 320 can turn the kelly drive bushing 319 and hence the kelly 318.The kelly drive bushing 319 can include an inside profile matching anoutside profile (e.g., square, hexagonal, etc.) of the kelly 318;however, with slightly larger dimensions so that the kelly 318 canfreely move up and down inside the kelly drive bushing 319.

As to a top drive example, the top drive 340 can provide functionsperformed by a kelly and a rotary table. The top drive 340 can turn thedrillstring 325. As an example, the top drive 340 can include one ormore motors (e.g., electric and/or hydraulic) connected with appropriategearing to a short section of pipe called a quill, that in turn may bescrewed into a saver sub or the drillstring 325 itself. The top drive340 can be suspended from the traveling block 311, so the rotarymechanism is free to travel up and down the derrick 314. As an example,a top drive 340 may allow for drilling to be performed with more jointstands than a kelly/rotary table approach.

In the example of FIG. 3 , the mud tank 301 can hold mud, which can beone or more types of drilling fluids. As an example, a wellbore may bedrilled to produce fluid, inject fluid or both (e.g., hydrocarbons,minerals, water, etc.).

In the example of FIG. 3 , the drillstring 325 (e.g., including one ormore downhole tools) may be composed of a series of pipes threadablyconnected together to form a long tube with the drill bit 326 at thelower end thereof. As the drillstring 325 is advanced into a wellborefor drilling, at some point in time prior to or coincident withdrilling, the mud may be pumped by the pump 304 from the mud tank 301(e.g., or other source) via a the lines 306, 308 and 309 to a port ofthe kelly 318 or, for example, to a port of the top drive 340. The mudcan then flow via a passage (e.g., or passages) in the drillstring 325and out of ports located on the drill bit 326 (see, e.g., a directionalarrow). As the mud exits the drillstring 325 via ports in the drill bit326, it can then circulate upwardly through an annular region between anouter surface(s) of the drillstring 325 and surrounding wall(s) (e.g.,open borehole, casing, etc.), as indicated by directional arrows. Insuch a manner, the mud lubricates the drill bit 326 and carries heatenergy (e.g., frictional or other energy) and formation cuttings to thesurface where the mud (e.g., and cuttings) may be returned to the mudtank 301, for example, for recirculation (e.g., with processing toremove cuttings, etc.).

The mud pumped by the pump 304 into the drillstring 325 may, afterexiting the drillstring 325, form a mudcake that lines the wellborewhich, among other functions, may reduce friction between thedrillstring 325 and surrounding wall(s) (e.g., borehole, casing, etc.).A reduction in friction may facilitate advancing or retracting thedrillstring 325. During a drilling operation, the entire drill string325 may be pulled from a wellbore and optionally replaced, for example,with a new or sharpened drill bit, a smaller diameter drill string, etc.As mentioned, the act of pulling a drill string out of a hole orreplacing it in a hole is referred to as tripping. A trip may bereferred to as an upward trip or an outward trip or as a downward tripor an inward trip depending on trip direction.

As an example, consider a downward trip where upon arrival of the drillbit 326 of the drill string 325 at a bottom of a wellbore, pumping ofthe mud commences to lubricate the drill bit 326 for purposes ofdrilling to enlarge the wellbore. As mentioned, the mud can be pumped bythe pump 304 into a passage of the drillstring 325 and, upon filling ofthe passage, the mud may be used as a transmission medium to transmitenergy, for example, energy that may encode information as in mud-pulsetelemetry.

As an example, mud-pulse telemetry equipment may include a downholedevice configured to effect changes in pressure in the mud to create anacoustic wave or waves upon which information may modulated. In such anexample, information from downhole equipment (e.g., one or more modulesof the drillstring 325) may be transmitted uphole to an uphole device,which may relay such information to other equipment for processing,control, etc.

As an example, telemetry equipment may operate via transmission ofenergy via the drillstring 325 itself. For example, consider a signalgenerator that imparts coded energy signals to the drillstring 325 andrepeaters that may receive such energy and repeat it to further transmitthe coded energy signals (e.g., information, etc.).

As an example, the drillstring 325 may be fitted with telemetryequipment 352 that includes a rotatable drive shaft, a turbine impellermechanically coupled to the drive shaft such that the mud can cause theturbine impeller to rotate, a modulator rotor mechanically coupled tothe drive shaft such that rotation of the turbine impeller causes saidmodulator rotor to rotate, a modulator stator mounted adjacent to orproximate to the modulator rotor such that rotation of the modulatorrotor relative to the modulator stator creates pressure pulses in themud, and a controllable brake for selectively braking rotation of themodulator rotor to modulate pressure pulses. In such example, analternator may be coupled to the aforementioned drive shaft where thealternator includes at least one stator winding electrically coupled toa control circuit to selectively short the at least one stator windingto electromagnetically brake the alternator and thereby selectivelybrake rotation of the modulator rotor to modulate the pressure pulses inthe mud.

In the example of FIG. 3 , an uphole control and/or data acquisitionsystem 362 may include circuitry to sense pressure pulses generated bytelemetry equipment 352 and, for example, communicate sensed pressurepulses or information derived therefrom for process, control, etc.

The assembly 350 of the illustrated example includes alogging-while-drilling (LWD) module 354, a measuring-while-drilling(MWD) module 356, an optional module 358, a roto-steerable system andmotor 360, and the drill bit 326.

The LWD module 354 may be housed in a suitable type of drill collar andcan contain one or a plurality of selected types of logging tools. Itwill also be understood that more than one LWD and/or MWD module can beemployed, for example, as represented at by the module 356 of thedrillstring assembly 350. Where the position of an LWD module ismentioned, as an example, it may refer to a module at the position ofthe LWD module 354, the module 356, etc. An LWD module can includecapabilities for measuring, processing, and storing information, as wellas for communicating with the surface equipment. In the illustratedexample, the LWD module 354 may include a seismic measuring device.

The MWD module 356 may be housed in a suitable type of drill collar andcan contain one or more devices for measuring characteristics of thedrillstring 325 and the drill bit 326. As an example, the MWD tool 354may include equipment for generating electrical power, for example, topower various components of the drillstring 325. As an example, the MWDtool 354 may include the telemetry equipment 352, for example, where theturbine impeller can generate power by flow of the mud; it beingunderstood that other power and/or battery systems may be employed forpurposes of powering various components. As an example, the MWD module356 may include one or more of the following types of measuring devices:a weight-on-bit measuring device, a torque measuring device, a vibrationmeasuring device, a shock measuring device, a stick slip measuringdevice, a direction measuring device, and an inclination measuringdevice.

FIG. 3 also shows some examples of types of holes that may be drilled.For example, consider a slant hole 372, an S-shaped hole 374, a deepinclined hole 376 and a horizontal hole 378.

As an example, a drilling operation can include directional drillingwhere, for example, at least a portion of a well includes a curved axis.For example, consider a radius that defines curvature where aninclination with regard to the vertical may vary until reaching an anglebetween about 30 degrees and about 60 degrees or, for example, an angleto about 90 degrees or possibly greater than about 90 degrees.

As an example, a directional well can include several shapes where eachof the shapes may aim to meet particular operational demands. As anexample, a drilling process may be performed on the basis of informationas and when it is relayed to a drilling engineer. As an example,inclination and/or direction may be modified based on informationreceived during a drilling process.

As an example, deviation of a bore may be accomplished in part by use ofa downhole motor and/or a turbine. As to a motor, for example, adrillstring can include a positive displacement motor (PDM).

As an example, a system may be a steerable system and include equipmentto perform method such as geosteering. As an example, a steerable systemcan include a PDM or of a turbine on a lower part of a drillstringwhich, just above a drill bit, a bent sub can be mounted. As an example,above a PDM, MWD equipment that provides real time or near real timedata of interest (e.g., inclination, direction, pressure, temperature,real weight on the drill bit, torque stress, etc.) and/or LWD equipmentmay be installed. As to the latter, LWD equipment can make it possibleto send to the surface various types of data of interest, including forexample, geological data (e.g., gamma ray log, resistivity, density andsonic logs, etc.).

The coupling of sensors providing information on the course of a welltrajectory, in real time or near real time, with, for example, one ormore logs characterizing the formations from a geological viewpoint, canallow for implementing a geosteering method. Such a method can includenavigating a subsurface environment, for example, to follow a desiredroute to reach a desired target or targets.

As an example, a drillstring can include an azimuthal density neutron(ADN) tool for measuring density and porosity; a MWD tool for measuringinclination, azimuth and shocks; a compensated dual resistivity (CDR)tool for measuring resistivity and gamma ray related phenomena; one ormore variable gauge stabilizers; one or more bend joints; and ageosteering tool, which may include a motor and optionally equipment formeasuring and/or responding to one or more of inclination, resistivityand gamma ray related phenomena.

As an example, geosteering can include intentional directional controlof a wellbore based on results of downhole geological loggingmeasurements in a manner that aims to keep a directional wellbore withina desired region, zone (e.g., a pay zone), etc. As an example,geosteering may include directing a wellbore to keep the wellbore in aparticular section of a reservoir, for example, to minimize gas and/orwater breakthrough and, for example, to maximize economic productionfrom a well that includes the wellbore.

Referring again to FIG. 3 , the wellsite system 300 can include one ormore sensors 364 that are operatively coupled to the control and/or dataacquisition system 362. As an example, a sensor or sensors may be atsurface locations. As an example, a sensor or sensors may be at downholelocations. As an example, a sensor or sensors may be at one or moreremote locations that are not within a distance of the order of aboutone hundred meters from the wellsite system 300. As an example, a sensoror sensor may be at an offset wellsite where the wellsite system 300 andthe offset wellsite are in a common field (e.g., oil and/or gas field).

As an example, one or more of the sensors 364 can be provided fortracking pipe, tracking movement of at least a portion of a drillstring,etc.

As an example, the system 300 can include one or more sensors 366 thatcan sense and/or transmit signals to a fluid conduit such as a drillingfluid conduit (e.g., a drilling mud conduit). For example, in the system300, the one or more sensors 366 can be operatively coupled to portionsof the standpipe 308 through which mud flows. As an example, a downholetool can generate pulses that can travel through the mud and be sensedby one or more of the one or more sensors 366. In such an example, thedownhole tool can include associated circuitry such as, for example,encoding circuitry that can encode signals, for example, to reducedemands as to transmission. As an example, circuitry at the surface mayinclude decoding circuitry to decode encoded information transmitted atleast in part via mud-pulse telemetry. As an example, circuitry at thesurface may include encoder circuitry and/or decoder circuitry andcircuitry downhole may include encoder circuitry and/or decodercircuitry. As an example, the system 300 can include a transmitter thatcan generate signals that can be transmitted downhole via mud (e.g.,drilling fluid) as a transmission medium.

As an example, one or more portions of a drillstring may become stuck.The term stuck can refer to one or more of varying degrees of inabilityto move or remove a drillstring from a bore. As an example, in a stuckcondition, it might be possible to rotate pipe or lower it back into abore or, for example, in a stuck condition, there may be an inability tomove the drillstring axially in the bore, though some amount of rotationmay be possible. As an example, in a stuck condition, there may be aninability to move at least a portion of the drillstring axially androtationally.

As to the term “stuck pipe”, the can refer to a portion of a drillstringthat cannot be rotated or moved axially. As an example, a conditionreferred to as “differential sticking” can be a condition whereby thedrillstring cannot be moved (e.g., rotated or reciprocated) along theaxis of the bore. Differential sticking may occur when high-contactforces caused by low reservoir pressures, high wellbore pressures, orboth, are exerted over a sufficiently large area of the drillstring.Differential sticking can have time and financial cost.

As an example, a sticking force can be a product of the differentialpressure between the wellbore and the reservoir and the area that thedifferential pressure is acting upon. This means that a relatively lowdifferential pressure (delta p) applied over a large working area can bejust as effective in sticking pipe as can a high differential pressureapplied over a small area.

As an example, a condition referred to as “mechanical sticking” can be acondition where limiting or prevention of motion of the drillstring by amechanism other than differential pressure sticking occurs. Mechanicalsticking can be caused, for example, by one or more of junk in the hole,wellbore geometry anomalies, cement, keyseats or a buildup of cuttingsin the annulus.

FIG. 4 shows an example of an environment 401 that includes asubterranean portion 403 where a rig 410 is positioned at a surfacelocation above a bore 420. In the example of FIG. 4 , various wirelinesservices equipment can be operated to perform one or more wirelinesservices including, for example, acquisition of data from one or morepositions within the bore 420.

In the example of FIG. 4 , the bore 420 includes drillpipe 422, a casingshoe, a cable side entry sub (CSES) 423, a wet-connector adaptor 426 andan openhole section 428. As an example, the bore 420 can be a verticalbore or a deviated bore where one or more portions of the bore may bevertical and one or more portions of the bore may be deviated, includingsubstantially horizontal.

In the example of FIG. 4 , the CSES 423 includes a cable clamp 425, apackoff seal assembly 427 and a check valve 429. These components canprovide for insertion of a logging cable 430 that includes a portion 432that runs outside the drillpipe 422 to be inserted into the drillpipe422 such that at least a portion 434 of the logging cable runs insidethe drillpipe 422. In the example of FIG. 4 , the logging cable 430 runspast the wet-connect adaptor 426 and into the openhole section 428 to alogging string 440.

As shown in the example of FIG. 4 , a logging truck 450 (e.g., awirelines services vehicle) can deploy the wireline 430 under control ofa system 460. As shown in the example of FIG. 4 , the system 460 caninclude one or more processors 462, memory 464 operatively coupled to atleast one of the one or more processors 462, instructions 466 that canbe, for example, stored in the memory 464, and one or more interfaces468. As an example, the system 460 can include one or moreprocessor-readable media that include processor-executable instructionsexecutable by at least one of the one or more processors 462 to causethe system 460 to control one or more aspects of equipment of thelogging string 440 and/or the logging truck 450. In such an example, thememory 464 can be or include the one or more processor-readable mediawhere the processor-executable instructions can be or includeinstructions. As an example, a processor-readable medium can be acomputer-readable storage medium that is not a signal and that is not acarrier wave.

FIG. 4 also shows a battery 470 that may be operatively coupled to thesystem 460, for example, to power the system 460. As an example, thebattery 470 may be a back-up battery that operates when another powersupply is unavailable for powering the system 460 (e.g., via a generatorof the wirelines truck 450, a separate generator, a power line, etc.).As an example, the battery 470 may be operatively coupled to a network,which may be a cloud network. As an example, the battery 470 can includesmart battery circuitry and may be operatively coupled to one or morepieces of equipment via a SMBus or other type of bus.

As an example, the system 460 can be operatively coupled to a clientlayer 480. In the example of FIG. 4 , the client layer 480 can includefeatures that allow for access and interactions via one or more privatenetworks 482, one or more mobile platforms and/or mobile networks 484and via the “cloud” 486, which may be considered to include distributedequipment that forms a network such as a network of networks. As anexample, the system 460 can include circuitry to establish a pluralityof connections (e.g., sessions). As an example, connections may be viaone or more types of networks. As an example, connections may beclient-server types of connections where the system 460 operates as aserver in a client-server architecture. For example, clients may log-into the system 460 where multiple clients may be handled, optionallysimultaneously.

As to types of measurements, these can include, for example, one or moreof resistivity, gamma ray, density, neutron porosity, spectroscopy,sigma, magnetic resonance, elastic waves, pressure, and sample data(e.g., as may be acquired while drilling to enable timely quantitativeformation evaluation).

As an example, data can include geochemical data. For example, considerdata acquired using X-ray fluorescence (XRF) technology, Fouriertransform infrared spectroscopy (FTIR) technology and/or wirelinegeochemical technology.

XRF technology involves emission of characteristic “secondary” (orfluorescent) X-rays from a material that has been excited by bombardmentwith high-energy X-rays or gamma rays. XRF technology may be implementedfor elemental analysis and chemical analysis, for example, as toresearch in geochemistry. As an example, in core analysis, XRFtechnology may be implemented to help determine mineral content. Forexample, elemental volumes may be inverted to mineral volumes byassuming certain standard formulae for mineral composition.

FTIR technology can involve analysis of an infrared spectrum ofabsorption, emission, photoconductivity or Raman scattering of a solid,liquid or gas. As an example, FTIR may be applied as a technique forquantitative mineralogical analysis of a sample of rock by measuring theeffect of midrange infrared radiation transmitted through the sample. Insuch an example, the radiation excites vibrations in the chemical bondswithin the mineral molecules at particular frequencies characteristic ofeach bond. The transmitted radiation may be compared with spectralstandards for a variety of minerals, for example, to determine abundanceof one or more minerals in the sample. As to sample preparation,consider, as an example, grinding a core plug to provide arepresentative sample that may be dispersed in a potassium bromidematrix and then subject to measurement and analysis.

As an example, one or more probes may be deployed in a bore via awireline or wirelines. As an example, a probe may emit energy andreceive energy where such energy may be analyzed to help determinemineral composition of rock surrounding a bore. As an example, nuclearmagnetic resonance may be implemented (e.g., via a wireline, downholeNMR probe, etc.), for example, to acquire data as to nuclear magneticproperties of elements in a formation (e.g., hydrogen, carbon,phosphorous, etc.).

As an example, lithology scanning technology may be employed to acquireand analyze data. For example, consider the LITHO SCANNER technologymarketed by Schlumberger Limited (Houston, Texas). As an example, aLITHO SCANNER tool may be a gamma ray spectroscopy tool. As an example,a workflow may include emission of neutrons by a pulsed neutrongenerator (PNG) of a tool to induce emission of gamma rays from aformation via interactions such as inelastic scattering interactions andthermal neutron capture interactions, which can produce gamma rays witha specific set of characteristic energies. In turn, gamma rays may bedetected by a LaBr₃:Ce scintillator coupled to a high-temperaturespectroscopy photomultiplier, producing signals that can be integrated,digitized, and processed by a high-performance pulse-height analyzer.Such an analyzer may determine, for example, pulse height (proportionalto energy) of individually detected gamma rays and accumulatepulse-height histograms (spectra) that tally counts versus pulse height.Spectra may be acquired, for example, during and after each neutronburst, which helps to enable separation of inelastic and capture gammarays. As an example, an individual spectrum may be decomposed into alinear combination of standard spectra from individual elements, whichcan involve adjustment for one or more environmental and/or electronicfactors. As an example, coefficients of linear combination of standardspectra may be converted to elemental weight fractions, for example, viaa modified geochemical oxides closure model, an inversion approach, etc.As to interpretation, various approaches may be implemented to generatemineralogy and lithologic fractions from the elemental concentrationlogs. As an example, a sequential spectral lithographic processingapproach may be used, which is based on the derivation of empiricalrelationships between elemental concentrations and mineralconcentrations. As another example, an iterative inversion technique maybe implemented (e.g., consider the TECHLOG QUANTI multicomponentinversion ELAN module).

As an example, a method may include acquiring data (e.g., and/orreceiving data) as measured via one or more techniques. Such techniquesmay include a micro-resistivity technique, a density and photoelectricfactor or index technique, an image calibration technique, a dielectricand conductivity dispersion technique, a neutron porosity technique, anultrasound technique, etc.

As an example, a workflow may utilize geochemical data, and optionallyother data, for one or more processes (e.g., stratigraphic modeling,basin modeling, completion designs, drilling, production, injection,etc.). As an example, lithology scanner tool data may be used in aworkflow or workflows that implement one or more frameworks

Table 1, below, shows some examples of data, which may be referred to as“log” data (e.g., well log data) that are associated with petrophysicaland rock physics properties calculation and analysis.

TABLE 1 Examples of Log Data Name Uses Gamma Ray (GR) Lithologyinterpretation, shale volume calculation, calculate clay volume,permeability calculation, porosity calculation, wave velocitycalculation, etc. Spontaneous Potential Lithology interpretation, Rw andRwe (SP) calculation, detect permeable zone, etc. Caliper (CALI) Detectpermeable zone, locate a bad hole Shallow Resistivity Lithologyinterpretation, finding (LLS and ILD) hydrocarbon bearing zone,calculate water saturation, etc. Deep Resistivity Lithologyinterpretation, finding (LLD and ILD) hydrocarbon bearing zone,calculate water saturation, etc. Density (RHOB) Lithologyinterpretation, finding hydrocarbon bearing zone, porosity calculation,rock physics properties (AI, SI, σ, etc.) calculation, etc. NeutronPorosity Finding hydrocarbon bearing zone, (NPHI) porosity calculation,etc. Sonic (DT) Porosity calculation, wave velocity calculation, rockphysics properties (AI, SI, σ, etc.) calculation, etc. Photoelectric(PEF) Mineral determination (for lithology interpretation)

FIGS. 1, 3 and 4 show various examples of equipment in various examplesof environments. As an example, one or more workflows may be implementedto perform operations using equipment in one or more environments. As anexample, a workflow may aim to understand an environment. As an example,a workflow may aim to drill into an environment, for example, to form abore defined by surrounding earth (e.g., rock, fluids, etc.). As anexample, a workflow may aim to acquire data from a downhole tooldisposed in a bore where such data may be acquired via a drilling tool(e.g., as part of a bottom hole assembly) and/or a wireline tool. As anexample, a workflow may aim to support a bore, for example, via casing.As an example, a workflow may aim to fracture an environment, forexample, via injection of fluid. As an example, a workflow may aim toproduce fluids from an environment via a bore. As an example, a workflowmay utilize one or more frameworks that operate at least in part via acomputer (e.g., a computing device, a computing system, etc.).

As mentioned, a log can be a well log. A well log can be, for example, aseries of measurements versus depth or time, or both, of one or morephysical quantities in or around a well. A log can be a recording ofinformation as acquired via one or more sensors. As mentioned, as to agamma ray sensor, gamma rays may be detected by a LaBr₃:Ce scintillatorcoupled to a high-temperature spectroscopy photomultiplier, producingsignals that can be integrated, digitized, and processed by ahigh-performance pulse-height analyzer. In such an example, a well logcan be a record (e.g., a recording) of sensor signal-based output.Transmission of signals and/or signal-based output from a device may bevia fiber, wire and/or wireless machinery. For example, circuitry may beutilized that includes one or more wires and/or fibers that can transmitsignals and/or signal-based output electrically and/or optically. As towireless transmission, one or more antennas may be utilized that canreceive and/or transmit electromagnetic energy that includes signalsand/or signal-based output. As an example, wireless transmission may bevia a medium such as a drilling fluid (e.g., mud, etc.). In such anexample, mud pulses may be utilized in a process known as mud-pulsetelemetry.

As an example, a log may be a depth series of data. For example,consider a log as a depth series of data with respect to true verticaldepth (TVD). TVD can be defined as the vertical distance from a point ina well to a point at the surface, which may be associated with a pieceof equipment (e.g., elevation of the rotary kelly bushing (RKB) alsoknown as a rotary bushing or kelly drive bushing such as the kelly drivebushing 319 of FIG. 3 , etc.). TVD is a type of depth measurement thatcan be used by a driller; while, another type is measured depth (MD).TVD can be utilized in determining pressures, which are caused in partby the hydrostatic head of fluid in a wellbore. Measured depth (MD), dueto intentional and/or unintentional curves in a wellbore tends to belonger than true vertical depth (TVD). As an example, in a frameworksuch as the PETREL framework, various depth tracks may exist for aplurality of wells, which may be given in standard sea level truevertical depth (SSTVD) where sea level is a common reference elevationfor each of the wells, which may start at different depths and havedifferent scales. As an example, one or more well logs may be accessedwhere data are given as a function of MD. In such an example, wherecoordinates of corresponding well trajectories are known (e.g.,accessible), a method can include transforming the well logs from beinga function of MD to being a function of TVD, for example, as TVD belowsea level (BSL or b.s.l.). As an example, for a geographic region,elevation of formation tops may varies by hundreds of meters where anSSTVD-based scale can facilitate comparisons between “common” featuresin well logs (e.g., via a propagation process, etc.). While SSTVD ismentioned, the acronym TVDSS may be utilized to represent TVD minus theelevation above mean sea level of a depth reference point of a well(e.g., TVD subsea). As mentioned, a depth reference point may be a kellydrive bushing or another portion of well-related equipment (e.g., drillfloor, etc.).

As explained, as a well may deviate from vertical, there may be ameasured depth (MD) for a point measured along a path of a wellbore anda true vertical depth (TVD) as an absolute vertical distance between adatum and a point in the wellbore. A datum may be selected from variousdata such as ground level (GL), drilling rig floor (DF), rotary table(RT), kelly bushing (KB or RKB), mean sea level (MSL), etc.

As an example, a method may implement a process referred to as elevationcorrection. Such a process may involve using a compensating factor tobring measurements to a common datum or reference plane. Well logheaders of well log data files can include an elevation such as that ofa drilling rig's kelly drive bushing and, for example, height of riglocation above sea level, so that well log depths can be elevationcorrected to sea level.

As an example, a method can include transforming logs to a particulardepth series form that provides for propagation. For example, atransformation may utilize a common reference and/or a common scale. Ininstances where RKB or other well-related equipment variations areminimal for a plurality of wells, a common start depth may be suitable;whereas, where variations exist a sea level or other standard type ofelevation may be utilized as a reference. As an example, where scaletransformations are made, a method can include interpolation such thatupsampling and/or downsampling may occur to make comparisons using acommon sample per unit depth factor. As an example, interpolation mayinclude spline fitting and/or one or more other techniques. As anexample, one or more logs may be pre-processed as part of a propagationprocess workflow where pre-processing may aim to facilitate propagationof a formation top from one log to another.

FIG. 5 shows an example set of logs (e.g., well logs) 500 and an inputmarker 510 as being an example of a user picked marker on one of thelogs in the set of logs. In FIG. 5 , the one log is referred to as beinga “picked” well or “picked” well log where “picked” refers to a processof picking a portion of a log as corresponding to a geological featuresuch as, for example, a formation feature such as, for example, aformation top (e.g., a boundary of a layer of a geological regionthrough which the well passes). A method such as the method 210 of FIG.2 may be utilized to propagate the picked marker 510 (e.g., a definedpick) to one or more of the other logs in the set of logs, which arelabeled as “to be analyzed”.

FIG. 6 shows an example of a process 600 that includes choosing a searchrange that is centered around a picked marker 610 such that a pattern isdefined that can be utilized in one or more matching attempts (e.g.,correlations, etc.). As shown in the example of FIG. 6 , a range may besymmetric and of the order of meters such as, for example, approximately200 m above a marker and approximately 200 m below a marker. In theexample of FIG. 6 , the upper dimension is shown as Z1 and the lowerdimension is shown as Z2 where various equations indication that Z1 andZ2 can be equal or different, where Z1 may be greater than Z2 or whereZ2 may be greater than Z1. As an example, greater than or equal toequations may be utilized. As an example, a range may be asymmetric withrespect to a picked marker. In the example of FIG. 6 , the upper limitof the depth search range is indicated as 612 and the lower limit of thedepth search range is indicate as 614. In the example of FIG. 6 , Z1 isequal to Z2 such that the picked marker 610 is in the middle of thedepth search range.

In the example of FIG. 6 , the depth of the search range can be truevertical depth or another suitable depth for purposes of propagation. Asan example, increasing search range can increase accuracy at the expenseof runtime and/or computational resources. Various trials performedutilized 200 m as illustrated in the example of FIG. 6 for Z1 and Z2. Asmentioned, the selected range can define a pattern or motif that amethod can attempt to match (e.g., correlate, etc.). As an example, alog can be a depth series, for example, sensor-based data referencedwith respect to depth, which may be a true vertical depth, a measureddepth, etc.

As an example, a method can include transforming a depth dimension. Forexample, consider a method that transforms a measured depth to a truevertical depth or other type of depth (e.g., an elevation referenceddepth, etc.). As an example, where a well is substantially vertical,measured depth (MD) may correspond to true vertical depth (TVD);whereas, for a deviated well, MD and TVD can differ (e.g., consider ahorizontal portion of a deviated well).

As an example, a method may utilize a multidimensional approach. Forexample, consider log data that is stored in association withthree-dimensional coordinates. As an example, a method can includeutilizing one or more sensor-based datum for a depth. For example,consider a horizontal portion of a well where material properties may berelatively constant in rock bounding the wellbore of the well. In suchan example, one or more values may be averaged and assigned a particulardepth. As explained, one or more of various approaches can be utilizedto provide logs with respect to depth (e.g., a log as a depth series ofsensor-based measurement values).

FIG. 7 shows an example of a method 700 that involves propagation of apicked marker. In the example of FIG. 7 , a FastDTW approach may beutilized. As shown, a position of a marker in a parent well log 720 isutilized to find a position for a marker in a child well log 740. As thetwo wells of the two well logs 720 and 740 are different wells withdifferent trajectories in a subsurface geologic region, a formation topcan be at different depths, for example, at depths that depend oncoordinates such as latitude and longitude at a surface location. In theexample of FIG. 7 , the two well logs 720 and 740 can be depth seriesalong a common depth dimension (e.g., true vertical depth, an elevationreferenced depth, etc.). As shown, the marker of a formation top is at adeeper depth in the child well log 740 (e.g., a child as the marker isderived from the parent well log 720). Thus, where the depth searchrange is over a common depth range (e.g., a common TVD range), theformation of that formation top can be a descending formation in adirection from the well of the well log 720 to the well of the well log740.

As to computational demands, computing a DTW demands O(N²) in general.Fast techniques for computing DTW include PrunedDTW, SparseDTW, FastDTW,and the MultiscaleDTW. A common task, retrieval of similar time series,can be accelerated by using lower bounds such as LB_Keogh orLB_Improved. As to FastDTW, consider Salvador et al., “FastDTW: TowardAccurate Dynamic Time Warping in Linear Time and Space”. KDD Workshop onMining Temporal and Sequential Data, pp. 70-80, 2004, which isincorporated by reference herein.

The aforementioned FastDTW algorithm can be a linear and accurateapproximation of dynamic time warping (DTW). The FastDTW algorithm canimplement a multilevel approach that recursively projects a warp pathfrom a coarser resolution to a current resolution and refines it. Whilequadratic time and space complexity of DTW has limited its use torelatively small time series data sets, FastDTW can be run on relativelylarger data sets. FastDTW can be, for example, an order of magnitudefaster than DTW and it can complement one or more existing indexingmethods that speed up time series similarity search and classification.Theoretically, FastDTW can be linear in time and space complexity.

A FastDTW mechanism can utilize a multilevel approach that includesvarious operations such as, for example, coarsening, projection andrefinement. As to coarsening, it can shrink a time series into a smallertime series that represents a curve as accurately as possible with fewerdata points. As to projection, it can find a minimum-distance warp pathat a lower resolution, and use that warp path as an initial guess for ahigher resolution's minimum-distance warp path. As to refinement, it canrefine the warp path projected from a lower resolution through localadjustments of the warp path.

An example of a function for FastDTW is presented below in the form ofexample pseudocode below:

Function FastDTW( ) Input: X—a TimeSeries of length |X| Y—a TimeSeriesof length |Y| radius—distance to search outside of the projected warppath from the previous resolution when refining the warp path Output: 1)A min. distance warp path between X and Y 2) The warped path distancebetween X and Y  1| // The min size of the coarsest resolution.  2|Integer minTSsize = radius+2  3|  4| IF (|X| ≤ minTSsize OR |Y| ≤minTSsize)  5| {  6|  // Base Case: for a very small time series run  7| // the full DTW algorithm.  8|  RETURN DTW(X, Y)  9|} 10| ELSE 11| {12|  // Recursive Case: Project the warp path from 13|  // a coarserresolution onto the current 14|  // current resolution. Run DTW along15|  // the projected path (and also ‘radius’ cells 16|  // from theprojected path). 17|  TimeSeries shrunkX = X.reduceByHalf( ) 18| TimeSeries shrunkY = Y.reduceByHalf( ) 19| 20|  WarpPath lowResPath =21|   FastDTW(shrunkX, shrunkY, radius) 22| 23|  SearchWindow window =24|   ExpandedResWindow(lowResPath, X, Y, 25|    radius) 26| 27|  RETURNDTW(X, Y, window) 28|}

As shown in the foregoing example, the input to the function includestwo time series and the radius parameter while the output of thefunction is a warp path and the distance between the two time seriesalong that warp path. Line 2 determines the minimum length of a timeseries at the lowest resolution. This size can be dependent on theradius parameter and can determine the smallest possible resolution sizefor which decreasing the resolution further would be likely unproductive(e.g., a full dynamic time warping may be demanded at more than oneresolution).

Again, FIG. 7 shows that a method can include propagating from one logto another, which may commence locally and extend globally. As anexample, a method can include choosing a way to pick a search range. Forexample, consider using the location wells to create an adaptive searchrange. Such an approach can include computing a Minimum Spanning Tree(MST) on locations of wells.

A minimum spanning tree (MST) or minimum weight spanning tree is asubset of edges of a connected, edge-weighted (un)directed graph thatconnects vertices together, for example, without cycles and with aminimum possible total edge weight. A MST may be defined as a spanningtree whose sum of edge weights is as small as possible. More generally,an edge-weighted undirected graph (not necessarily connected) has aminimum spanning forest, which is a union of the minimum spanning treesfor its connected.

An algorithm fora MST can be the Borůvka's algorithm, which proceeds ina sequence of stages where, in each stage, called Borůvka step, itidentifies a forest F involving the minimum-weight edge incident to eachvertex in the graph G, and then forms the graph as the input to the nextstep. Each Borůvka step takes linear time and, as the number of verticesis reduced by at least half in each step, Borůvka's algorithm takes O(mlog n) time.

Another example algorithm is Prim's algorithm, which grows the MST (T)one edge at a time. Initially, T includes an arbitrary vertex. In eachstep, T is augmented with a least-weight edge (x,y) such that x is in Tand y is not yet in T. By a “Cut” property, edges added to T are in theMST. The Prim's algorithm run-time is either O(m log n) or O(m+n log n),depending on the data-structures used.

Other examples of algorithms include Kruskal's algorithm; thereverse-delete algorithm, which is the reverse of Kruskal's algorithm; acomparison model, in which allowed operations on edge weights arepairwise comparisons; a combination of Borůvka's algorithm and thereverse-delete algorithm; and the soft heap, an approximate priorityqueue as Chazelle's algorithm.

FIG. 8 shows an example of a graphical user interface (GUI) 800 thatincludes a well identified by an open circle with a thick border, whichmay be selected using a graphical control feature. Other wells areindicated by small black circles. The GUI 800 shows a selected well(e.g., a parent well or seed well) and a set of wells to be analyzed,which may be referred to as child wells or children of the selected well(e.g., parent or seed). As an example, a method can include generating aminimum spanning tree (MST) on the wells identified by the small blackcircles, for example, where the well identified by the larger opencircle may be considered to be a seed well (e.g., parent).

As an example, a method can include, from an input or seed log,propagating to other wells (well logs). As mentioned, such an approachmay be proximity based as to neighbors or, for example, such an approachmay aim to perform comparisons in parallel or in another manner where acoupled structure may be generated, optionally without linear progressfrom one well to another well.

FIG. 9 shows an example of a graphical user interface (GUI) 900 thatincludes a map of the region of wells as in the GUI 800 of FIG. 8 where,for example, a method can adjust a search range as a picked marker ispropagated (see, e.g., FIG. 11 , etc.).

FIG. 10 shows an example of a tree in various stages or phases 1010,1020 and 1030 where a seed can propagate via one or more branches wheresuch one or more branches may further propagate. As shown in theselection stage 1010, a seed (e.g., or parent) is selected. In a firstpropagation stage 1020, the seed is propagated to four neighboringwells. In a second propagation stage 1030, three of the children arefurther propagated to one or more of their “outward” neighbors (e.g., ina direction generally outward from each of the children with respect tothe parent or seed, which can define an inward direction.

FIG. 11 illustrates examples of method 1110 and 1130 where the method1130 includes adjusting a search range (e.g., a search depth range) oneor more times as a picked marker is propagated from one log of one wellto one or more other logs or one or more other logs. As shown in FIG. 11with respect to the method 1110, an approach may not take advantage of awell location and may miss the mapping for a number of wells (see, e.g.,how the feature of the logs is below the search zone for the rightmostthree logs). As shown in FIG. 11 with respect to the method 1130, anapproach may include propagating a top to a neighbor and then using thenew top to adjust a search range for the neighbor's top. Such anapproach may be performed iteratively. In such an example, a “top” canbe a marker (e.g., type of marker).

The method 1130 of FIG. 13 involves adjusting a search zone (e.g., asearch depth range) using a marker, which can be a propagated markerthat marks a portion of a log that includes sensor-based data withrespect to a dimension such as a depth (e.g., true vertical depth,etc.). The method 1110 can include an inherent assumption that theformation top identified does not change to an extent that it would beoutside of the search zone of the seed log of the seed well. As such,for the formation top to be properly propagated, a relatively largesearch zone is demanded. Where such a search zone is of equal dimensionsabove and below the seed marker of the seed log of the seed well,computational demands can increase and a risk of not being able topropagate still exists (e.g., a feature of a log being at a depth thatis outside of the fixed search zone). In contrast, the method 1130 canadjust the search zone iteratively. Such an approach may retaindimensions of a search zone. While the example in FIG. 13 as to themethod 1130 shows a search zone that is iteratively adjusted to deeperdepths, a method can adjust a search zone iteratively to shallowerdepths and/or deeper depths in a manner that depends on where a markeris propagated in a parent well log (e.g., in a chain of parent to child,parent to child, etc.). As an example, well log markers (e.g., formationtops) may be referred to generationally such as grandparent, parent,child, grandchild, etc. As explained, a parent may be propagated to oneor more children or may be a terminal parent where no further well logsare to be analyzed.

As an example, a method can optionally adjust one or more dimensions ofa search zone (e.g., a search depth range). For example, where awell-to-well distance increases, a search zone dimension or dimensionsmay be increased and where a well-to-well distance decreases, a searchzone dimension or dimensions may be decreased. As an example, a methodcan include utilizing depth data for a plurality of prior propagatedmarkers, optionally with distances, to determine a search zone dimensionor dimensions. For example, consider a standard deviation calculationthat can generate a standard deviation, which, if small or decreasing,can reduce a search zone dimension or dimensions. In contrast, where astandard deviation is larger or increasing, can increase a search zonedimension or dimensions.

Referring again to FIGS. 8 and 9 , FIG. 9 shows an example of a map ofthe region of wells as in FIG. 8 where a method can adjust a searchrange as a picked marker is propagated. The map of FIG. 9 illustratessuch an approach from the perspective of logs that are “linked” viapropagation.

As explained, while an algorithm can propagate a single pick, geologycan be complex such that multiple picks make for a more robust result.In other words, a single pick may be confounded when propagating in anenvironment where there is changing geology and/or one or more otherthings an algorithm may not figure out without specific domainknowledge.

As an example, a method can include a multiple pick approach, which maybe iterative. For example, after a single pick has been input, a methodmay output predictions for logs (see, e.g., the method 210 of FIG. 2 ).Such predictions may be of varying accuracy and/or certainty. As anexample, a method can include issuing one or more notifications where,for example, a notification may be issued automatically to a user (e.g.,a machine, a human, etc.) to focus on the log where choosing a revisedpick would most improve the predictions and re-propagate the well topsafter the user's revisions. Such an approach can, in turn, minimize anumber of picks a human may have to make. As explained with respect tothe method 210 of FIG. 2 , a method can include a computation block forcomputing a confidence score for one or more predictions.

FIG. 12 shows an example of a method 1210 that may be implemented forcomputing a confidence metric (e.g., a confidence score). As shown, themethod includes a prediction block 1214 for predicting formation toplocations using a FastDTW algorithm, a computation block 1218 forcomputing a confidence score using a warp distance as determined via theFastDTW algorithm, and an output block 1222 for outputting a confidencescore. As an example, a confidence score can be utilized for one or morepurposes. For example, consider rendering a map with confidence scoremarkers, indicators, etc. As another example, consider rendering one ormore logs with formation tops where one or more for the formation topsmay include a graphical indicator of confidence (e.g., uncertainty,certainty, etc.). Such an approach can facilitate a quality controlworkflow that aims to generate a model of a subsurface geographic regionwith respect to layers defined at least in part via formation tops usinglog information.

In the method 1210, where FastDTW is utilized, a warp distance iscomputed that measures a difference between log signatures (e.g., welllog signatures). As shown, “a” can be a normalizing constant. Asindicated, where the warp distance (“warp_dist”) tends to zero,confidence tends to one and where the warp distance tends to infinity,confidence tends to 0. Such scores may be transformed to percentages(e.g., 0% and 100% confidence).

As an example, a method can include issuing a notification for a user toreview a lowest confidence well. In such an example, once this pick hasbeen confirmed or revised by the user, the method may iterate byrepeating various actions to generate predictions stemming from this newpick. As mentioned, such iterations may continue until one or morecriteria are met.

As an example, a method can include continuously monitoring log data fora field as they are available (e.g., from a data storage, sensorequipment, a network, etc.) where the data are received via an interfaceof a computer that can utilize one or more functions (e.g., FastDTW,etc.), and optionally input acquired via a graphical user interface(GUI), to generate a result (or results), and signaling a device toperform an operation based at least in part on the result (or results).In such an example, the operation can be a field operation that is to beperformed by at least one piece of field equipment. In such an example,the result (or results) can pertain to the Earth as the log data includeinformation about the Earth (e.g., as acquired by one or more sensors).More accurate and/or more timely information about the Earth can allowfor signaling to a device or devices to operate more effectively withrespect to the Earth (e.g., injection, production, measurement,drilling, casing, fracturing, etc.), which may be for the purpose ofresource extraction from the Earth (e.g., extraction of fluid such ashydrocarbon fluid).

As an example, a method can include a multiple pick approach (e.g.,upfront approach). In such an example, consider a number of input picksY upfront (from older interpretations for example), which may be in thetens of picks (e.g., 10, 20, 30, 40, 50, 60, etc.). In such a method, aselection may be made as to a number of predictions to make for eachunpicked wells (e.g., consider a number X less than approximately 10).In such an approach, for each unpicked well, the method can find its Xnearest picked wells. Using this information, the method can create anMST for each of the Y input logs such that each unpicked logs will be inX MSTs. In such an example, the method can then propagate from each MSTand, for example, pick the highest confidence wells.

As an example, a method can include visualization via one or more GUIs.For example, by taking a path along a branch in a tree, it is possibleto visualize changes in depth of logs, which may help a user morereadily identify mistakes or other errors.

FIG. 13 shows an example of randomly ordered logs 1310 versus an exampleof MST order of logs 1330 where the logs are illustrated with respect totrue vertical depth (TVD). As shown, the MST order of logs 1330, whencompared to the randomly ordered logs 1310, provides for a morecomprehensible assessment of variations that may occur in formationstops for a plurality of wells in a region. As an example, a GUI caninclude a graphical control that can present logs according to a MSTorder such as in the example of FIG. 13 .

FIG. 14 shows an example of a confidence map GUI 1400 where hatchinglevels represents confidence levels (e.g., consider red, yellow, andgreen or other techniques) for a plurality of wells with one picked well(see open circle with thick border). The GUI 1400 can correspond to amap such as one of the maps of FIG. 8 or FIG. 9 . In the example of FIG.14 , five confidence levels are illustrated; noting that more levels orfewer levels may be utilized (e.g., as part of a method parameter, whichmay be selectable). The levels can be, for example, from 0 to 1 or 0 to100. The lowest level may be 0.0 to 0.2, the next level 0.2 to 0.4, thenext level 0.4 to 0.6, the next level 0.6 to 0.8 and the last level 0.8to 1.0. As shown, the wells with the highest level tend to be in aregion about the picked well. Where distance increases, in someinstances, the confidence level as to one or more propagated formationtops may decrease.

As shown, the GUI 1400 can provide an overview fora relatively largegeographic region with a relatively large number of wells. A user mayutilize such a GUI to relatively rapidly assess results of an automatedpropagation process. As an example, a user may utilize a graphicalcontrol of the GUI 1400 to select one of the wells and instruct acomputational framework to perform propagation utilizing the selectedone of the wells in addition to one or more prior selected wells. Forexample, in the example of the GUI 1400, a region includes various wellsthat have relatively low confidence levels. A user may select a well inthat region and then actuate a graphical control to cause thecomputational framework to propagate using that selected well incombination with the prior selected well and/or results therefrom. Forexample, consider a process whereby the newly selected well propagatesand determines a confidence score that can be compared to a priorcomputed confidence score to determine whether the newly selected wellprovides a more “confident” result. Such an approach may causepropagation (e.g., or one or more portions thereof) to terminate wherethe newly selected well does not improve the results as to one or morewells.

FIG. 15 shows an example of a confidence map GUI 1500 where the hatchingconfidence levels of FIG. 14 are shown for a plurality of wells with twopicked wells (see the two open circles with thick borders). As shown inthe example of FIG. 15 , the selection of an additional seed well hasimproved the confidence level of results in region of the map. A usermay inspect such confidence levels to determine whether to furtherprocess, edit, propagate, etc. As an example, a map may be generatedresponsive to execution of a method that includes one or more parentwells that propagate formation top(s) to one or more child wells (e.g.,children).

As an example, one or more GUIs may be animated. For example, as amethod executes via a computing device or computing system, informationas to propagation may be rendered to a display such that a user can seehow the method is propagating information. In such an example, thecomputing device or computing system may be interactive where a GUIallows a user to pick a feature on a log and then instruct the computingdevice or computing system to propagate that picked feature to one ormore other logs, which may be associated with other wells in a field. Asthe propagation occurs, confidence information (e.g., confidence levels,etc.) may be rendered to a display and the user may select a well basedat least in part on such information, for example, to cause thecomputing device or computing system to render a log associated withthat well (e.g., a well log). As an example, a side by side renderingmay be displayed such that a user can readily compare the log with thepicked feature to the log that has been subject to propagation. In suchan example, the user may decide to re-evaluate the propagation to thatlog and, for example, override by making a pick (e.g., a new pick, a newseed, etc.) for that log, which itself may optionally be propagated.Such a computer-implemented workflow can expedite an assessment of theEarth via acquired data (e.g., well log data). Such an assessment of theEarth can allow for issuing one or more signals to one or more devices,which may be field devices that can perform one or more operations atsurface and/or downhole.

As an example, a GUI may render real-time propagation information suchas connection links and confidence scores (e.g., levels, etc.) where theGUI can include a graphical control that may allow a user to terminatepropagation or one or more portions thereof. For example, where lowconfidence levels are rendered for connection links of a propagationprocess in real-time, a user may click on a connection link to terminateit. Such an approach can be akin to trimming a branch of a growing tree.Such an approach can expedite propagation processing as to quality ofresults. In such an approach, while the propagation process is running,a graphical control can allow for selecting another seed and commencingpropagation from that seed. For example, where a user terminates abranch (e.g., a connection link), the user may select a well of thatbranch or proximate thereto to be a seed. The process can then continue,for example, in parallel to the prior propagation process. As mentioned,one or more propagation processes may proceed in parallel. As anexample, a computational framework may include a graphical control thatcan control computation speed, for example, to allow a user tointervene. As an example, the graphical control may be a slider controlthat allows a user to speed up and slow down computation speed. As anexample, a graphical control can allow for pausing, rewinding andforwarding propagation.

FIG. 16 shows an example of a GUI 1600 with a map and logs. For example,a location or region may be selected on the map using a graphicalcontrol and one or more logs for one or more wells can be rendered.Where a region includes many wells, a scroll bar may be rendered to thedisplay such that a user can scroll across a number of logs. As anexample, the GUI 800 of FIG. 8 , the GUI 900 of FIG. 9 , etc., mayinclude functionality as explained with respect to the GUI 1600 of FIG.16 . As an example, such functionality may be operable during executionof a propagation process. For example, a user may utilize one or moreGUIs, graphical controls, etc., to assess results, “plant” a seed, etc.,while a propagation process executes. In such an approach, a user canperform various tasks (e.g., quality control, etc.) during execution ofa propagation process, which can make the user more productive (e.g.,reduce a user's non-productive time).

FIG. 17 shows an example of a GUI log view 1700. Various regions areidentified as including logs (e.g., wells with logs). Such a GUI may beutilized to determine wells, region boundaries, etc., for performing apropagation process. As an example, a user may perform a search for ageographic region, see search results as in the GUI log view 1700 ofFIG. 17 and then select one or more regions to perform propagation as toformation tops, etc.

FIGS. 18 to 41 show various examples of GUIs that illustrate variousactions, which can be a portion of one or more workflows. In the exampleGUIs of FIGS. 18 to 41 , various selections, actions, features, etc.,are identified. In the examples, various distances are provided, whichcan depend on particular wells, fields, etc.

FIG. 18 illustrates an example of a graphical user interface (GUI) 1800that includes various examples of logs for various wells where one ofthe wells is a selected well. The GUI 1800 includes a log panel and astrip panel where the log panel includes logs for a number of wells thatcorrespond to wells in the strip panel. The strip panel is shown asincluding more wells than in the log panel. The strip panel can includean adjustable window or adjustable windows where logs for wells in theone or more adjustable windows can be rendered in the log panel. Asshown some of the logs in the log panel include log information from oneor more logging techniques (e.g., from different sensors, etc.). In theexamples shown, the log panel includes a selected well with twoindividual logs, a well at a distance of 1.2 miles with a single log, awell at a distance of 4.5 miles with a single log, a well with adistance of 4.5 miles with three individual logs, a well with anunmarked distance with two individual logs, a well with an unmarkeddistance with a single log and a well with an unmarked distance with asingle log.

The GUI 1800 also includes various graphical controls such as, forexample, a propagate markers graphical control, a validate topsgraphical control, a validate “all” tops graphical control, and log typeselector graphical controls (e.g., “all”, gamma ray (GR), neutrondensity (ND), resistivity (Res), etc.). As shown, the GUI 1800 can bepart of a framework with a navigable architecture. As shown, the GUI1800 can include a depth view graphic, which can include one or moregraphical controls.

As an example, the GUI 1800 can be utilized to commence a method forpropagating markers. For example, one or more markers of the selectedwell (see star symbol) can be propagated to one or more of the otherwells via their corresponding one or more logs.

FIG. 19 illustrates an example of a graphical user interface (GUI) 1900that includes the logs and wells of FIG. 18 and a graphical controlassociated with one of the wells. Specifically, the graphical controlindicates that a particular well with a number of logs does not havedefined formation tops.

Formation tops can be defined via true vertical depths (TVD) in a well(e.g., measured in distance below a reference elevation) at which one ormore of various formations may be found; noting, that in the case of theglacial drift, the “top” is actually the bottom or base of the driftwhere “base of drift” is the depth of the bedrock surface at the well.Various wells may have associated data in digital form and/or in “paper”form. For example, an old well may have a log recorded on paper. Varioustypes of logs in one or more forms may be accessed from one or moredatabases, which can include one or more digital databases. As anexample, a GUI can include one or more graphical controls for accessingdigital log data for rendering to a display, for example, as log datawith respect to depth (e.g., with respect to a reference elevation,etc.).

As an example, a database can include information such as elevations,formation codes, formation tops, method obtained, and reference pointcodes. Such information can be provided along with a locationidentifier, which may be a name, latitude and longitude, an AmericanPetroleum Institute (API) number, etc. As an example, as to elevations,consider a parameter “Elev_KB”, which is a drilling rig's kelly bushing(see, e.g., the kelly drive bushing 319) elevation, or a parameter“Elev_DF, which is a drilling floor elevation (e.g., of a drilling rigor a derrick). As to formation codes, consider, for example, AmericanAssociation of Petroleum Geologists codes (AAPG_Cd) or one or more othertypes of codes. As to method obtained, such information can includelogging related information (e.g., logging tool, etc.).

In the example of FIG. 19 , the well with the three individual logs maybe a well for which formation top identification (e.g., picking) has notyet occurred (e.g., or fully occurred) and, for example, for which anautomated approach is to be implemented. For example, an automatedapproach can include propagating known formation log information from aselected well to a well with one or more logs where formation tops havenot been identified or for which a re-identification is desired and/orfurther identification is desired. As an example, a tool may haveassociated processing equipment that may perform an initial, automatedtop picking. Where such information is available, the GUI 1900 mayinclude a graphical control for rendering of previously pickedinformation as to one or more formation tops. As explained, apropagation approach can act to “spread” across a geographic subsurfaceregion to identify how a formation top or formation tops vary spatiallyover the geographic subsurface region. Such an approach can be, forexample, a neighbor to neighbor approach.

FIG. 20 illustrates an example of a graphical user interface (GUI) 2000that includes the logs and wells of FIG. 18 and a graphical control forpropagating information of the selected well to one or more of the otherwells.

FIG. 21 illustrates an example of a graphical user interface (GUI) 2100that includes the logs and wells of FIG. 18 where graphics indicate howinformation is propagated from the selected well to the other wells.Specifically, the GUI 2100 shows how formation tops of the selected wellhave been utilized as seeds for propagation across a subsurfacegeographic region that includes wells with logs where the logs areanalyzed in a neighbor to neighbor approach to identify formation tops.As shown in FIG. 21 , the GUI 2100 provides spatial information, asindicate via distance indicators for each of the wells (e.g.,neighboring distances).

The GUI 2100 also provides information as to particular issues that mayexist. For example, where uncertainty of a formation top of a well foundby propagation is high, the GUI 2100 can render an indicator such as,for example, an exclamation point. In the example of FIG. 21 , the GUI2100 also renders well markers as thicker where uncertainty is an issue.While line thickness is illustrated, one or more other techniques may beutilized (e.g., color, blinking, etc.) to highlight one or moreuncertainty issues. While the GUI 2100 is shown in black and white,colors may be utilized to represent formation information. For example,different layers may be represented via different colors.

FIG. 22 illustrates an example of a graphical user interface (GUI) 2200that includes the logs and wells and information of FIG. 21 andconfidence information. FIG. 22 also shows selection of a graphicalcontrol for viewing gamma ray (GR) log data. For example, the confidenceinformation (e.g., or uncertainty information or certainty information)may be for GR logs. For example, the propagation approach may utilize GRlog information and the GUI 2200 can provide for rendering informationassociated with GR-based propagation.

FIG. 23 illustrates an example of a graphical user interface (GUI) 2300that includes the logs and wells and information of FIG. 22 and agraphical control for rendering information about tops of one of thewells. Specifically, the GUI 2300 shows that one of the wells has itstops defined by automated propagation.

FIG. 24 illustrates an example of a graphical user interface (GUI) 2400that includes the logs and wells and information of FIG. 22 and agraphical control for rendering uncertainty information about tops ofone of the wells. Specifically, the GUI 2400 shows that one of the wellshas two formation tops with high levels of uncertainty (e.g., above anuncertainty limit value). In the example of FIG. 24 , the values aregiven in percent as 48% and 52%, which can be certainty values forrespective depths of two formation tops. As an example, a limit may beset at approximately 75% certainty. Certainty may be a confidence score(see, e.g., FIG. 12 ) that is transformed from a range of 0 to 1 to arange of 0% to 100%. A certainty limit may be a confidence score limitthat is transformed from a value within a range from 0 to 1 to a valuein a range from 0% to 100%. The GUI 2400 of FIG. 24 also shows theparticular well in the strip panel as having a thicker line thatindicates that one or more formation tops are lacking certainty (e.g.,confidence). In the strip panel, a star indicates the selected well inthe log panel, which is identified with a star.

FIG. 25 illustrates an example of a graphical user interface (GUI) 2500that includes the logs and wells of FIG. 24 and a graphical control thatselects a portion of a log of one of the wells. Specifically, aformation top region of the well that is 1.2 miles from the well withthe star is selected using the graphical control. The graphical controlcan allow user to edit (e.g., adjust) one or more formation tops, forexample, to move a formation top up or down, to delete a formation top,to add a formation top, etc.

FIG. 26 illustrates an example of a graphical user interface (GUI) 2600that includes the logs and wells of FIG. 25 and the graphical controlthat is used to select another portion of the log of the one of thewells. As shown in the example of FIG. 26 , one or more connections areadjusted responsive to use of the graphical control to edit the well. Asshown, connections can be adjusted in multiple directions, from a parentwell to a child well. The GUIs 2500 and 2600 illustrate a process ofmoving a formation top downwardly to a deeper depth, which may beperformed with reference to one or more logs. For example, in the GUI2500 the formation top that is adjusted is in a valley of the log and inthe GUI 2600 the formation top is moved to correspond approximately to apeak of the log. In the GUIs 2500 and 2600, a user can visually inspectthe formation tops and connections as well as one or more logs todetermine that an adjustment is appropriate. The workflow in the GUIs2500 and 2600 shows that one formation layer is made thicker via theadjustment of the selected formation top and that an adjacent formationlayer is made thinner (i.e., the one immediately below) while thecorresponding connections to the parent and the child are adjustedaccordingly. While the workflow shows enlarging a formation layer andshrinking an adjacent formation layer or one or more other formationlayer/formation top adjustments may be performed.

FIG. 27 illustrates an example of a graphical user interface (GUI) 2700that includes the logs and wells of FIG. 26 and a graphical control forrendering information about a modified top of the one of the wells. Asshown, the graphical control renders information concerning one of eightformation tops being modified on a particular date (e.g., and optionallya particular time, a particular user, etc.).

FIG. 28 illustrates an example of a graphical user interface (GUI) 2800that includes various examples of logs for various wells of FIG. 27 anda graphical control for editing one or more connections between wells.Specifically, in the example of FIG. 28 , a zone is selected that is aconnection zone defined by an upper connection between formation tops inlogs of two wells and a lower connection between formation tops in logsof two wells.

FIG. 29 illustrates an example of a graphical user interface (GUI) 2900that includes various examples of logs for various wells of FIG. 28 andthe graphical control for deleting one or more connections betweenwells. For example, consider a right click of a mouse that can causerendering of a menu with menu items as options for control actions whereone action can be to delete a selected zone.

FIG. 30 illustrates an example of a graphical user interface (GUI) 3000that includes various examples of logs for various wells of FIG. 29where one or more connections are deleted between wells according to thedeletion of the selected zone per a workflow as illustrated in the GUIs2800 and 2900.

FIG. 31 illustrates an example of a graphical user interface (GUI) 3100that includes the logs and wells of FIG. 26 and a graphical control fora flattening operation. The graphical control is illustrated as beingperformed with respect to the depth view of the GUI 3100.

FIG. 32 illustrates an example of a graphical user interface (GUI) 3200that includes the logs and wells of FIG. 31 as adjusted per theflattening operation. Specifically, the depth view shows seven formationlayers with corresponding formation tops. The graphical control hasselected the formation top of the third layer, which can be anapproximate formation bottom position of the second layer. In theflattening operation, the logs of the wells are adjusted such that theformation top of the third layer is rendered at a common position suchthat a horizontal line can be drawn across the logs, for example, toallow for visual inspection, quality control, etc., of the formation topselected (e.g., the boundary between the second and third formationlayers).

FIG. 33 illustrates an example of a graphical user interface (GUI) 3300that includes the logs and wells of FIG. 32 and a graphical control fora restoration of a depth view operation.

FIG. 34 illustrates an example of a graphical user interface (GUI) 3400that includes the logs and wells of FIG. 33 as restored per therestoration of the depth view operation.

FIG. 35 illustrates an example of a graphical user interface (GUI) 3500that includes the logs and wells of FIG. 26 and a graphical control foruncertainty information, which can be confidence information (e.g.,confidence scores, etc.).

FIG. 36 illustrates an example of a graphical user interface (GUI) 3600that includes the logs and wells of FIG. 35 and rendered uncertaintyinformation. As shown in the example of FIG. 36 , the information may berendered for a formation top and/or a formation top region. Variousexamples of values are shown, which, as explained, can be based onconfidence scores (see, e.g., FIG. 12 ). Such information can facilitatequality control operations that can aim to more accurately determinelocations of formations in a subsurface geographic region. As anexample, a user may select a log and perform editing of one or moreformation tops. As mentioned, a tool may include equipment that canperform an automated process or, for example, a framework may includeone or more features for processing a log to identify a formation topand a location of the formation top. A user may perform a workflow thatutilizes the GUI 3600 for one or more actions to more accurate generatelocations of formation tops in a subsurface geographic region thatincludes a plurality of wells.

FIG. 37 illustrates an example of a graphical user interface (GUI) 3700that includes the logs and wells of FIG. 36 and a graphical control forvalidating tops in the rendered view. As mentioned, the strip panel caninclude an adjustable window where wells in the window are rendered inthe log panel. In the example of FIG. 37 , the validation control can beutilized to validate the wells in the log panel. A workflow may includemoving the window of the strip panel such that another view is renderedto the log panel with one or more different wells. A workflow may be aniterative process where a user can adjust a strip panel window toperform operations as to a plurality of formation tops for a pluralityof wells in a geographic region.

FIG. 38 illustrates an example of a graphical user interface (GUI) 3800that includes examples of logs and wells in a log panel that correspondto logs and wells in a strip panel that includes an adjustable window.

FIG. 39 illustrates an example of a graphical user interface (GUI) 3900that includes examples of logs and wells in the log panel of FIG. 38that correspond to logs and wells in the strip panel of FIG. 38 and agraphical control for adjusting the adjustable window.

FIG. 40 illustrates an example of a graphical user interface (GUI) 4000that includes examples of logs and wells in a log panel that correspondto logs and wells in the strip panel of FIG. 38 as selected viaadjustment of the adjustable window.

FIG. 41 illustrates an example of a graphical user interface (GUI) 4100that includes examples of logs and wells and a graphical control foraccessing details of a selected well. For example, such a graphicalcontrol can include accessing details and rendering the details to theGUI 4100 and/or another GUI.

As an example, a map can include markers that change colors to representconfidence (e.g., consider red, yellow and green). And, as an example, auser may intervene during propagation. For example, if a user sees toomany red and yellow confidence levels being rendered for wells, agraphical control of a user interface may be utilized to halt (e.g.,pause) a propagation process. The user may then examine various wellsand associated logs (see, e.g., one or more of FIGS. 18 to 41 ). A usermay identify a problem with a particular log of a particular well andthen rectify that problem (e.g., via an adjustment) and then recommencethe propagation. For example, a particular branch may include a parentthat causes children to be of lower confidence. A user may adjust one ormore formation tops of that parent and cause an automated propagationroutine to recommence with the adjusted one or more formation tops ofthe parent. A GUI may then render confidence levels to a display wherethe user can determine if the adjustment to the parent rectified theconfidence problems with the children (e.g., child wells as shown inFIG. 10 , etc.).

As an example, as to propagation, one or more approaches may be taken,which may be in 2D or 3D and may differ for each pick. Propagation maybe via a line to another well or expanding surface or volume or other.While nearest and/or direct neighbor are mentioned, an approach mayutilize one or more other criteria for expanding. For example, considertwo leases that are adjacent and where a propagation workflow is to belimited to wells of one of the leases. In such an approach, a leaseboundary may be a criterion for propagation. As an example, a naturalboundary such as a river, a fault, etc., may be utilized as a boundarycriterion (or criteria) for propagation.

As an example, as to parallel execution, a 1D log may have multiplepicks that propagate in parallel and, for multiple wells, there may bemultiple seeds that propagate in parallel. As mentioned, informationgenerated during parallel execution can cross-inform, whether how toadjust one pick based on another pick and/or how to propagate from oneseed based on how another seed is propagating (noting that one seed maybe in one formation and the other seed in another formation where theyshould propagate to a common boundary at a common depth).

As an example, a computational framework may be suitable for use inanalyzing data for a field with many wells and formations that haveunknown boundaries. As an example, data can be 1D data where a value isprovided at a depth. As an example, data may be or include gamma raydata. As an example, an approach can be robust to differences in gammaray data from tool to tool, noting that some scaling or otherpre-processing may be employed.

As an example, a computational framework may include features that canprovide feedback to a user such as a human user. For example, a systemcan tell a user that a pick was a poor one or that a pick was a goodone. As an example, a user (e.g., a human user) may assign confidence toa pick and a system can give feedback to see if the user was accurateand/or the system may utilize the assigned confidence in makingpropagation decisions/boundary decisions/etc. In such an example, a GUIcan include a field or other control tool that can facilitate assignmentof a confidence (e.g., at the time of a pick, etc.).

As an example, a method can include, for a marker on a well logassociated with a well, propagating the marker to a plurality of otherwell logs associated with respective wells. In such an example,propagating can include an adjustable depth search range. As an example,such an adjustable depth search range may be automatically adjustable.As an example, propagating can include computing a warp distance. Insuch an example, a method can include computing a confidence score basedat least in part on the warp distance. As an example, propagating caninclude implementing a FastDTW algorithm that computes the warpdistance. As an example, a warp distance can be a measure of adifference between data of two of well logs.

As an example, well logs can include data acquired by at least onesensor operatively coupled to a downhole tool. Such data may be signaldata and/or signal-data based output of a device (e.g., a scientificinstrument, a sensor device, field equipment, etc.).

As an example, well logs can include data with respect to depth where,for example, depth is true vertical depth.

As an example, a method can include computing confidence scores forindividual correlations between pairs of well logs. In such an example,the method can include identifying a lowest confidence score asassociated with one of the wells and issuing a notification thatidentifies the one of the wells. In such an example, the method caninclude defining a marker on the well log for the one of the wells andfor the marker on the well log associated with the well, propagating themarker to a plurality of other well logs associated with respectivewells.

As an example, a method can include propagating that includes generatinga minimum spanning tree (MST). In such an example, a marker can be aseed of the MST. As an example, a marker may be based on informationreceived via a GUI that is rendered to a display operatively coupled toa computing device or system where, for example, interpretation of oneor more well logs may be performed, optionally with input from a userthat can utilize an input mechanism (e.g., a touchscreen, a voicecommand, a mouse, a stylus, etc.) to generate information (e.g., a pick,a marker, etc.) for a well log or well logs rendered to the display.

As an example, a method can include, for a plurality of markers on awell log associated with a well, propagating the plurality of markers toa plurality of other well logs associated with respective wells. In suchan example, the plurality of markers may correspond to different typesof well log measurements (e.g., different types of sensors, etc.). As anexample, a plurality of markers can correspond to different truevertical depths of a well log.

As an example, a method can include, for a plurality of markers on aplurality of well logs associated with a plurality of wells, propagatingthe plurality of markers to a plurality of other well logs associatedwith respective wells.

As an example, a system can include a processor; memory operativelycoupled to the processor; and processor-executable instructions storedin the memory to instruct the system to, for a marker on a well logassociated with a well, propagate the marker to a plurality of otherwell logs associated with respective wells. In such an example, thesystem can include instructions to render a graphical user interface(GUI) to a display of the system where the GUI may include features torender one or more logs and, for example, to zoom-in, zoom-out, scrollthrough logs, etc., and to identify and mark one or more portions of alog (e.g., to pick a portion of a log). In such an example, a markedportion of a log may be utilized by the system to propagate and, forexample, to output results of such propagation, which can provide for abetter and/or more timely understanding of the Earth. As an example, thesystem may include an interface that can issue one or more signals toone or more pieces of equipment that can instruct the equipment toperform one or more actions as to a portion of the Earth as may berepresented at least in part by one or more logs (e.g., as subjected topropagation, etc.).

As an example, one or more computer-readable storage media can includecomputer-executable instructions executable to instruct a computingsystem to, for a marker on a well log associated with a well, propagatethe marker to a plurality of other well logs associated with respectivewells.

FIG. 42 shows an example of a method 4210 that includes a receptionblock 4214 for receiving a marker on a well log for a well in ageographic region; and a propagation block 4218 for iterativelypropagating the marker automatically to a plurality of well logs forother wells in the geographic region. The method 4210 can also include arender block 4222 for rendering a graphical user interface to a display(see, e.g., FIGS. 5 to 41 , etc.).

The method 4210 is shown in FIG. 42 in association with variouscomputer-readable media (CRM) blocks 4215, 4219, and 4223. Such blocksgenerally include instructions suitable for execution by one or moreprocessors (or cores) to instruct a computing device or system toperform one or more actions. While various blocks are shown, a singlemedium may be configured with instructions to allow for, at least inpart, performance of various actions of the method 4210. As an example,a CRM block can be a computer-readable storage medium that isnon-transitory, not a carrier wave and not a signal. As an example, suchblocks can include instructions that can be stored in memory and can beexecutable by one or more of processors. As an example, blocks may beprovided as one or more sets of instructions, for example, such as theone or more sets of instructions 466 of the system 460 of FIG. 4 .

As an example, a marker can be a formation top marker that marks aformation top of a formation in the geographic region where a wellincludes a wellbore that is defined at least part by a portion of theformation (e.g., a bore wall of the wellbore).

As an example, a method for propagating can include computing a warpdistance. Such a method can include computing a confidence score basedat least in part on the warp distance. As an example, a warp distancecan be computed using a computational algorithm such as a dynamic timewarping algorithm. Such computational algorithm can be applied, forexample, in a spatial domain that can be a multidimensional spatialdomain (e.g., 2D, 3D, etc.). For example, wellbores can be describedusing multidimensional coordinates in a 3D spatial domain that includesa depth dimension (e.g., a vertical depth dimension) where well log datacan be along a depth dimension scale and where wellbores may beseparated by one or more lateral dimensions. As an example, propagatingcan include implementing a fast dynamic time warping (FastDTW)computational algorithm using one or more processors to compute warpdistance. As an example, warp can be a measure of a difference betweendata of two well logs.

As an example, a method can include propagating that includes adjustingan adjustable depth search range. For example, consider determining anupper depth search range limit and a lower depth search range limitusing a depth of a propagated marker that is based on a received marker(e.g., a seed marker). Such a propagated marker may be directly orindirectly based on a received marker (e.g., depending on generationalrelationship). As an example, a method can include determining an upperdepth search range limit by adding an upper limit parameter value to adepth of a propagated marker and determining a lower depth search rangelimit by subtracting a lower limit parameter value from the depth of thepropagated marker. As an example, a method can include propagating apropagated marker that is based on a received marker to a feature of oneof a plurality of well logs where the feature is at a depth within theadjustable depth search range.

As an example, well logs can include data (e.g., values) with respect toa vertical depth. For example, a well log can include data valuesorganized with respect to vertical depth, which may be along a verticaldepth scale. Such a scale can be a linear vertical depth scale. In suchan example, an adjustable depth search range may move upwardly and/ordownwardly as a marker is propagate. For example, where the marker isfor a formation top, if the formation top descends from one well log toanother well log, the adjustable depth search range can be adjusted todescend downwardly. For example, if a depth search range spansapproximately 400 meters, for a well log X6, the range may be from 1200meters to 1600 meters and, for another well log X7, the range may befrom 1250 meters to 1650 meters. In such an example, the adjustabledepth search range has been adjusted by 50 meters. As to propagatedmarker based adjustment, consider a marker in the well log X6 being at1450 meters, which is 250 meters below 1200 meters and 150 meters above1600 meters. As the marker is not centered at 1400 meters, the depthsearch range for the well log X7 can be adjusted downwardly such that itis centered at 1450 meters. If a marker can be identified in the welllog X7, its depth can be utilized to make an adjustment in theadjustable search range for yet another well log (e.g., X8).

As an example, a method can include computing confidence scores forindividual correlations between pairs of well logs. In such an example,a method can include identifying a lowest confidence score as associatedwith a well (e.g., a well log) and issuing a notification thatidentifies the well. For example, consider the GUI 2400 of FIG. 24 ,which includes visual indicia that indicates that one or more confidencescores may be low. As an example, a lowest confidence score may beidentified such that a user can be notified of a worst case scenariothat may be deserving of attention. As explained with respect to theGUIs 1400 and 1500, a method can include receiving an additional marker(e.g., an additional seed), which may be utilized to improve confidencefor marker propagation (e.g., formation top identification) in ageographic region. As an example, a user may interact with a GUI tocause a system to receive an additional marker for a lowest confidencescore well (e.g., well log). As an example, a method can optionallyinclude identifying a lowest confidence score well (e.g., well log) andreceiving an additional marker as a seed where the seed can bepropagated to improve confidence within a geographic region.

As an example, a method can include propagating by generating a minimumspanning tree (MST). Such an approach can include receiving a marker ofa well log of a well and using the marker as a seed to generate the MSTwhere the MST includes other well (e.g., well logs) in a geographicregion. As an example, a method can include receiving a seed and growinga MST in a geographic region where propagation is to be performed, forexample, to identify depths of a top of a formation that spans at leasta portion of the geographic region.

As an example, a method can include rendering a map to a display wherethe map includes graphical indicators for wells. In such an example,each of the graphical indicators can indicate a well location and aconfidence metric for a propagated well log marker (e.g., optionallyrendered in real-time during execution of a propagation method). As anexample, a confidence metric can be a color, a blinking rate, a shadinglevel, a number, etc.

As an example, a method can include rendering a graphical user interfaceto a display where the graphical user interface includes well logs thatinclude markers that indicate formation tops. As an example, such amethod can include, responsive to receipt of an instruction to renderconfidence metrics, rendering at least one confidence metric inassociation with at least one of the markers that indicates at least oneof the formation tops.

As an example, a system can include a processor; memory operativelycoupled to the processor; and processor-executable instructions storedin the memory to instruct the system to: receive a marker on a well logfor a well in a geographic region; and iteratively propagate the markerautomatically to a plurality of well logs for other wells in thegeographic region.

As an example, one or more computer-readable storage media can includecomputer-executable instructions executable to instruct a computingsystem to: receive a marker on a well log for a well in a geographicregion; and iteratively propagate the marker automatically to aplurality of well logs for other wells in the geographic region.

As an example, a workflow may be associated with variouscomputer-readable medium (CRM) blocks. Such blocks generally includeinstructions suitable for execution by one or more processors (or cores)to instruct a computing device or system to perform one or more actions.As an example, a single medium may be configured with instructions toallow for, at least in part, performance of various actions of aworkflow. As an example, a computer-readable medium (CRM) may be acomputer-readable storage medium that is non-transitory, not a carrierwave and not a signal. As an example, blocks may be provided as one ormore sets of instructions, for example, such as the one or more sets ofinstructions 466 of the system 460 of FIG. 4 .

FIG. 43 shows components of an example of a computing system 4300 and anexample of a networked system 4310. The system 4300 includes one or moreprocessors 4302, memory and/or storage components 4304, one or moreinput and/or output devices 4306 and a bus 4308. In an exampleembodiment, instructions may be stored in one or more computer-readablemedia (e.g., memory/storage components 4304). Such instructions may beread by one or more processors (e.g., the processor(s) 4302) via acommunication bus (e.g., the bus 4308), which may be wired or wireless.The one or more processors may execute such instructions to implement(wholly or in part) one or more attributes (e.g., as part of a method).A user may view output from and interact with a process via an I/Odevice (e.g., the device 4306). In an example embodiment, acomputer-readable medium may be a storage component such as a physicalmemory storage device, for example, a chip, a chip on a package, amemory card, etc. (e.g., a computer-readable storage medium).

In an example embodiment, components may be distributed, such as in thenetwork system 4310. The network system 4310 includes components 4322-1,4322-2, 4322-3, . . . 4322-N. For example, the components 4322-1 mayinclude the processor(s) 4302 while the component(s) 4322-3 may includememory accessible by the processor(s) 4302. Further, the component(s)4302-2 may include an I/O device for display and optionally interactionwith a method. The network may be or include the Internet, an intranet,a cellular network, a satellite network, etc.

As an example, a device may be a mobile device that includes one or morenetwork interfaces for communication of information. For example, amobile device may include a wireless network interface (e.g., operablevia IEEE 802.11, ETSI GSM, BLUETOOTH, satellite, etc.). As an example, amobile device may include components such as a main processor, memory, adisplay, display graphics circuitry (e.g., optionally including touchand gesture circuitry), a SIM slot, audio/video circuitry, motionprocessing circuitry (e.g., accelerometer, gyroscope), wireless LANcircuitry, smart card circuitry, transmitter circuitry, GPS circuitry,and a battery. As an example, a mobile device may be configured as acell phone, a tablet, etc. As an example, a method may be implemented(e.g., wholly or in part) using a mobile device. As an example, a systemmay include one or more mobile devices.

As an example, a system may be a distributed environment, for example, aso-called “cloud” environment where various devices, components, etc.interact for purposes of data storage, communications, computing, etc.As an example, a device or a system may include one or more componentsfor communication of information via one or more of the Internet (e.g.,where communication occurs via one or more Internet protocols), acellular network, a satellite network, etc. As an example, a method maybe implemented in a distributed environment (e.g., wholly or in part asa cloud-based service).

As an example, information may be input from a display (e.g., consider atouchscreen), output to a display or both. As an example, informationmay be output to a projector, a laser device, a printer, etc. such thatthe information may be viewed. As an example, information may be outputstereographically or holographically. As to a printer, consider a 2D ora 3D printer. As an example, a 3D printer may include one or moresubstances that can be output to construct a 3D object. For example,data may be provided to a 3D printer to construct a 3D representation ofa subterranean formation. As an example, layers may be constructed in 3D(e.g., horizons, etc.), geobodies constructed in 3D, etc. As an example,holes, fractures, etc., may be constructed in 3D (e.g., as positivestructures, as negative structures, etc.).

Although only a few example embodiments have been described in detailabove, those skilled in the art will readily appreciate that manymodifications are possible in the example embodiments. Accordingly, allsuch modifications are intended to be included within the scope of thisdisclosure as defined in the following claims. In the claims,means-plus-function clauses are intended to cover the structuresdescribed herein as performing the recited function and not onlystructural equivalents, but also equivalent structures. Thus, although anail and a screw may not be structural equivalents in that a nailemploys a cylindrical surface to secure wooden parts together, whereas ascrew employs a helical surface, in the environment of fastening woodenparts, a nail and a screw may be equivalent structures. It is theexpress intention of the applicant not to invoke 35 U.S.C. § 112,paragraph 6 for any limitations of any of the claims herein, except forthose in which the claim expressly uses the words “means for” togetherwith an associated function.

What is claimed is:
 1. A method comprising: determining confidencescores for individual correlations between pairs of well logs of aplurality of well logs for a plurality of wells in a geographic region;identifying a lowest confidence score of the confidence scores asassociated with a well of the plurality of wells; issuing a notificationindicative of the well; defining a marker on a well log for the well;iteratively propagating the marker automatically to two or more welllogs of the plurality of well logs for other wells of the plurality ofwells; rendering a graphical user interface to a display, wherein thegraphical user interface comprises well logs that comprise markers thatindicate formation tops; and responsive to receipt of an instruction torender confidence metrics, rendering at least one confidence metric inassociation with at least one of the markers that indicates at least oneof the formation tops.
 2. The method of claim 1 wherein the markers thatindicate the formation tops comprise the marker propagated to the two ormore well logs.
 3. The method of claim 1 wherein the propagatingcomprises computing a warp distance.
 4. The method of claim 3 comprisingcomputing a confidence score of the confidence scores based at least inpart on the warp distance.
 5. The method of claim 3 wherein thepropagating comprises implementing a fast dynamic time warping (FastDTW)computational algorithm using one or more processors to compute the warpdistance.
 6. The method of claim 3 wherein the warp distance is ameasure of a difference between data of two well logs of the pluralityof well logs.
 7. The method of claim 1 wherein the propagating comprisesadjusting an adjustable depth search range, wherein the adjustingcomprises determining an upper depth search range limit and a lowerdepth search range limit using a depth of a propagated marker that isbased on the marker.
 8. The method of claim 1 wherein the plurality ofwell logs comprises data with respect to a vertical depth.
 9. The methodof claim 1 wherein the propagating comprises generating a minimumspanning tree (MST), wherein the marker is a seed of the MST.
 10. Themethod of claim 1 comprising rendering a map to the display, wherein themap comprises graphical indicators for at least a portion of the otherwells, wherein each of the graphical indicators indicates a welllocation for a propagated well log marker.
 11. The method of claim 1wherein the at least one confidence metric comprises a confidence scoreof the confidence scores.
 12. A system comprising: a processor; memoryoperatively coupled to the processor; and processor-executableinstructions stored in the memory to instruct the system to: determineconfidence scores for individual correlations between pairs of well logsof a plurality of well logs for a plurality of wells in a geographicregion; identify a lowest confidence score of the confidence scores asassociated with a well of the plurality of wells; issue a notificationindicative of the well; define a marker on a well log for the well; anditeratively propagate the marker automatically to two or more well logsof the plurality of well logs for other wells of the plurality of wells;render a graphical user interface to a display, wherein the graphicaluser interface comprises well logs that comprise markers that indicateformation tops; and responsive to receipt of an instruction to renderconfidence metrics, render at least one confidence metric in associationwith at least one of the markers that indicates at least one of theformation tops.
 13. The system of claim 12 wherein theprocessor-executable instructions instruct the system to determine awarp distance corresponding to a difference between data of two welllogs of the plurality of well logs.
 14. The system of claim 13 whereinthe processor-executable instructions instruct the system to determine aconfidence score of the confidence scores based on the warp distance.15. The system of claim 12 wherein the markers that indicate theformation tops comprise the marker propagated to the two or more welllogs, wherein the at least one confidence metric comprises a confidencescore of the confidence scores.
 16. One or more tangiblecomputer-readable storage media comprising computer-executableinstructions configured thereupon that are executable to instruct acomputing system to perform a method comprising: determining confidencescores for individual correlations between pairs of well logs of aplurality of well logs for a plurality of wells in a geographic region;identifying a lowest confidence score of the confidence scores asassociated with a well of the plurality of wells; issuing a notificationindicative of the well; receiving a marker on a well log for the well;iteratively propagating the marker automatically to two or more welllogs of the plurality of well logs for other wells of the plurality ofwells; rendering a graphical user interface to a display, wherein thegraphical user interface comprises well logs that comprise markers thatindicate formation tops; and responsive to receipt of an instruction torender confidence metrics, rendering at least one confidence metric inassociation with at least one of the markers that indicates at least oneof the formation tops.
 17. The one or more tangible computer-readablestorage media of claim 16 wherein the plurality of well logs comprisesdata with respect to a vertical depth.
 18. The one or more tangiblecomputer-readable storage media of claim 16 wherein the propagatingcomprises adjusting an adjustable depth search range or generating aminimum spanning tree (MST).
 19. The one or more tangiblecomputer-readable storage media of claim 16 wherein the propagatingcomprises the adjusting, wherein the adjusting comprises determining anupper depth search range limit and a lower depth search range limitusing a depth of a propagated marker that is based on the marker. 20.The one or more tangible computer-readable storage media of claim 16wherein the markers that indicate the formation tops comprise the markerpropagated to the two or more well logs, wherein the at least oneconfidence metric comprises a confidence score of the confidence scores.